AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Pielage, L.; Schmidle, P.; Marschall, B.; Risse, B.
Interactive High-Quality Skin Lesion Generation using Diffusion Models for VR-based Dermatological Education Proceedings Article
In: Int Conf Intell User Interfaces Proc IUI, pp. 878–897, Association for Computing Machinery, 2025, ISBN: 979-840071306-4 (ISBN).
Abstract | Links | BibTeX | Tags: Deep learning, Dermatology, Diffusion Model, diffusion models, Digital elevation model, Generative AI, Graphical user interfaces, Guidance Strategies, Guidance strategy, Image generation, Image generations, Inpainting, Interactive Generation, Medical education, Medical Imaging, Simulation training, Skin lesion, Upsampling, Virtual environments, Virtual Reality
@inproceedings{pielage_interactive_2025,
title = {Interactive High-Quality Skin Lesion Generation using Diffusion Models for VR-based Dermatological Education},
author = {L. Pielage and P. Schmidle and B. Marschall and B. Risse},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001923208&doi=10.1145%2f3708359.3712101&partnerID=40&md5=639eec55b08a54ce813f7c1016c621e7},
doi = {10.1145/3708359.3712101},
isbn = {979-840071306-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int Conf Intell User Interfaces Proc IUI},
pages = {878–897},
publisher = {Association for Computing Machinery},
abstract = {Malignant melanoma is one of the most lethal forms of cancer when not detected early. As a result, cancer screening programs have been implemented internationally, all of which require visual inspection of skin lesions. Early melanoma detection is a crucial competence in medical and dermatological education, and it is primarily trained using 2D imagery. However, given the intrinsic 3D nature of skin lesions and the importance of incorporating additional contextual information about the patient (e.g., skin type, nearby lesions, etc.), this approach falls short of providing a comprehensive and scalable learning experience. A potential solution is the use of Virtual Reality (VR) scenarios, which can offer an effective strategy to train skin cancer screenings in a realistic 3D setting, thereby enhancing medical students' awareness of early melanoma detection. In this paper, we present a comprehensive pipeline and models for generating malignant melanomas and benign nevi, which can be utilized in VR-based medical training. We use diffusion models for the generation of skin lesions, which we have enhanced with various guiding strategies to give educators maximum flexibility in designing scenarios and seamlessly placing lesions on virtual agents. Additionally, we have developed a tool which comprises a graphical user interface (GUI) enabling the generation of new lesions and adapting existing ones using an intuitive and interactive inpainting strategy. The tool also offers a novel custom upsampling strategy to achieve a sufficient resolution required for diagnostic purposes. The generated skin lesions have been validated in a user study with trained dermatologists, confirming the overall high quality of the generated lesions and the utility for educational purposes. © 2025 Copyright held by the owner/author(s).},
keywords = {Deep learning, Dermatology, Diffusion Model, diffusion models, Digital elevation model, Generative AI, Graphical user interfaces, Guidance Strategies, Guidance strategy, Image generation, Image generations, Inpainting, Interactive Generation, Medical education, Medical Imaging, Simulation training, Skin lesion, Upsampling, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Malignant melanoma is one of the most lethal forms of cancer when not detected early. As a result, cancer screening programs have been implemented internationally, all of which require visual inspection of skin lesions. Early melanoma detection is a crucial competence in medical and dermatological education, and it is primarily trained using 2D imagery. However, given the intrinsic 3D nature of skin lesions and the importance of incorporating additional contextual information about the patient (e.g., skin type, nearby lesions, etc.), this approach falls short of providing a comprehensive and scalable learning experience. A potential solution is the use of Virtual Reality (VR) scenarios, which can offer an effective strategy to train skin cancer screenings in a realistic 3D setting, thereby enhancing medical students' awareness of early melanoma detection. In this paper, we present a comprehensive pipeline and models for generating malignant melanomas and benign nevi, which can be utilized in VR-based medical training. We use diffusion models for the generation of skin lesions, which we have enhanced with various guiding strategies to give educators maximum flexibility in designing scenarios and seamlessly placing lesions on virtual agents. Additionally, we have developed a tool which comprises a graphical user interface (GUI) enabling the generation of new lesions and adapting existing ones using an intuitive and interactive inpainting strategy. The tool also offers a novel custom upsampling strategy to achieve a sufficient resolution required for diagnostic purposes. The generated skin lesions have been validated in a user study with trained dermatologists, confirming the overall high quality of the generated lesions and the utility for educational purposes. © 2025 Copyright held by the owner/author(s).
2024
Saddik, A. E.; Ghaboura, S.
The Integration of ChatGPT With the Metaverse for Medical Consultations Journal Article
In: IEEE Consumer Electronics Magazine, vol. 13, no. 3, pp. 6–15, 2024, ISSN: 21622248 (ISSN).
Abstract | Links | BibTeX | Tags: Chatbots, Computational Linguistics, Cutting edges, Diagnosis, Health care, Healthcare delivery, Healthcare environments, Human like, Immersive, Language Model, Medical diagnostic imaging, Medical Imaging, Medical services, Metaverses
@article{saddik_integration_2024,
title = {The Integration of ChatGPT With the Metaverse for Medical Consultations},
author = {A. E. Saddik and S. Ghaboura},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174844304&doi=10.1109%2fMCE.2023.3324978&partnerID=40&md5=ce0da4988d06258a1bc695e2d4ac4677},
doi = {10.1109/MCE.2023.3324978},
issn = {21622248 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Consumer Electronics Magazine},
volume = {13},
number = {3},
pages = {6–15},
abstract = {Recent years witnessed a promising synergy between healthcare and the Metaverse leading to the development of virtual healthcare environments. This convergence offers accessible and immersive healthcare experiences and holds the potential for transforming the delivery of medical services and enhancing patient outcomes. However, the reliance on specialist presence in the metaverse for medical support remains a challenge. On the other hand, the newly launched large language model chatbot, the ChatGPT of OpenAI, has emerged as a game-changer, providing human-like responses and facilitating interactive conversations. By integrating this cutting-edge language model with the Metaverse for medical purposes, we can potentially revolutionize healthcare delivery, enhance access to care, and increase patient engagement. This study proposes a new medical Metaverse model utilizing GPT-4 as a content creator, highlighting its potential, addressing challenges and limitations, and exploring various application fields. We conclude by outlining our ongoing efforts to transform this concept into a practical reality. © 2012 IEEE.},
keywords = {Chatbots, Computational Linguistics, Cutting edges, Diagnosis, Health care, Healthcare delivery, Healthcare environments, Human like, Immersive, Language Model, Medical diagnostic imaging, Medical Imaging, Medical services, Metaverses},
pubstate = {published},
tppubtype = {article}
}
Recent years witnessed a promising synergy between healthcare and the Metaverse leading to the development of virtual healthcare environments. This convergence offers accessible and immersive healthcare experiences and holds the potential for transforming the delivery of medical services and enhancing patient outcomes. However, the reliance on specialist presence in the metaverse for medical support remains a challenge. On the other hand, the newly launched large language model chatbot, the ChatGPT of OpenAI, has emerged as a game-changer, providing human-like responses and facilitating interactive conversations. By integrating this cutting-edge language model with the Metaverse for medical purposes, we can potentially revolutionize healthcare delivery, enhance access to care, and increase patient engagement. This study proposes a new medical Metaverse model utilizing GPT-4 as a content creator, highlighting its potential, addressing challenges and limitations, and exploring various application fields. We conclude by outlining our ongoing efforts to transform this concept into a practical reality. © 2012 IEEE.
2023
Wang, Z.; Joshi, A.; Zhang, G.; Ren, W.; Jia, F.; Sun, X.
Elevating Perception: Unified Recognition Framework and Vision-Language Pre-Training Using Three-Dimensional Image Reconstruction Proceedings Article
In: Proc. - Int. Conf. Artif. Intell., Human-Comput. Interact. Robot., AIHCIR, pp. 592–596, Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835036036-3 (ISBN).
Abstract | Links | BibTeX | Tags: 3D Model LLM, 3D modeling, 3D models, 3D Tech, 3d-modeling, Augmented Reality, Character recognition, Component, Computer aided design, Computer vision, Continuous time systems, Data handling, Generative AI, Image enhancement, Image Reconstruction, Image to Text Generation, Medical Imaging, Pattern recognition, Pre-training, Reconstructive Training, Text generations, Three dimensional computer graphics, Virtual Reality
@inproceedings{wang_elevating_2023,
title = {Elevating Perception: Unified Recognition Framework and Vision-Language Pre-Training Using Three-Dimensional Image Reconstruction},
author = {Z. Wang and A. Joshi and G. Zhang and W. Ren and F. Jia and X. Sun},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192837757&doi=10.1109%2fAIHCIR61661.2023.00105&partnerID=40&md5=0fe17cc622a9aa90e88b8c3e6a3bed3b},
doi = {10.1109/AIHCIR61661.2023.00105},
isbn = {979-835036036-3 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Proc. - Int. Conf. Artif. Intell., Human-Comput. Interact. Robot., AIHCIR},
pages = {592–596},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This research project explores a paradigm shift in perceptual enhancement by integrating a Unified Recognition Framework and Vision-Language Pre-Training in three-dimensional image reconstruction. Through the synergy of advanced algorithms from computer vision & language processing, the project tries to enhance the precision and depth of perception in reconstructed images. This innovative approach holds the potential to revolutionize fields such as medical imaging, virtual reality, and computer-aided design, providing a comprehensive perspective on the intersection of multimodal data processing and perceptual advancement. The anticipated research outcomes are expected to significantly contribute to the evolution of technologies that rely on accurate and contextually rich three-dimensional reconstructions. Moreover, the research aims to reduce the constant need for new datasets by improving pattern recognition through 3D image patterning on backpropagation. This continuous improvement of vectors is envisioned to enhance the efficiency and accuracy of pattern recognition, contributing to the optimization of perceptual systems over time. © 2023 IEEE.},
keywords = {3D Model LLM, 3D modeling, 3D models, 3D Tech, 3d-modeling, Augmented Reality, Character recognition, Component, Computer aided design, Computer vision, Continuous time systems, Data handling, Generative AI, Image enhancement, Image Reconstruction, Image to Text Generation, Medical Imaging, Pattern recognition, Pre-training, Reconstructive Training, Text generations, Three dimensional computer graphics, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This research project explores a paradigm shift in perceptual enhancement by integrating a Unified Recognition Framework and Vision-Language Pre-Training in three-dimensional image reconstruction. Through the synergy of advanced algorithms from computer vision & language processing, the project tries to enhance the precision and depth of perception in reconstructed images. This innovative approach holds the potential to revolutionize fields such as medical imaging, virtual reality, and computer-aided design, providing a comprehensive perspective on the intersection of multimodal data processing and perceptual advancement. The anticipated research outcomes are expected to significantly contribute to the evolution of technologies that rely on accurate and contextually rich three-dimensional reconstructions. Moreover, the research aims to reduce the constant need for new datasets by improving pattern recognition through 3D image patterning on backpropagation. This continuous improvement of vectors is envisioned to enhance the efficiency and accuracy of pattern recognition, contributing to the optimization of perceptual systems over time. © 2023 IEEE.
2021
Franchini, Silvia; Vitabile, Salvatore
Geometric Calculus Applications to Medical Imaging: Status and Perspectives Proceedings Article
In: Xambó-Descamps, Sebasti`a (Ed.): Systems, Patterns and Data Engineering with Geometric Calculi, pp. 31–46, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-74486-1.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics
@inproceedings{franchiniGeometricCalculusApplications2021,
title = {Geometric Calculus Applications to Medical Imaging: Status and Perspectives},
author = { Silvia Franchini and Salvatore Vitabile},
editor = { Sebasti{`a} {Xambó-Descamps}},
doi = {10.1007/978-3-030-74486-1_3},
isbn = {978-3-030-74486-1},
year = {2021},
date = {2021-01-01},
booktitle = {Systems, Patterns and Data Engineering with Geometric Calculi},
pages = {31--46},
publisher = {Springer International Publishing},
address = {Cham},
series = {SEMA SIMAI Springer Series},
abstract = {Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.},
keywords = {3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics},
pubstate = {published},
tppubtype = {inproceedings}
}
Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.
Franchini, Silvia; Terranova, Maria Chiara; Re, Giuseppe Lo; Galia, Massimo; Salerno, Sergio; Midiri, Massimo; Vitabile, Salvatore
In: Esposito, Anna; Faundez-Zanuy, Marcos; Morabito, Francesco Carlo; Pasero, Eros (Ed.): Progresses in Artificial Intelligence and Neural Systems, pp. 185–197, Springer, Singapore, 2021, ISBN: 9789811550935.
Abstract | Links | BibTeX | Tags: Bayesian optimization, Crohn's disease multi-level classification and grading, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, multi-level classifiers, Multiclass support vector machines, Supervised learning
@incollection{franchiniNovelSystemMultilevel2021,
title = {A Novel System for Multi-level Crohn's Disease Classification and Grading Based on a Multiclass Support Vector Machine},
author = { Silvia Franchini and Maria Chiara Terranova and Giuseppe Lo Re and Massimo Galia and Sergio Salerno and Massimo Midiri and Salvatore Vitabile},
editor = { Anna Esposito and Marcos {Faundez-Zanuy} and Francesco Carlo Morabito and Eros Pasero},
doi = {10.1007/978-981-15-5093-5_18},
isbn = {9789811550935},
year = {2021},
date = {2021-01-01},
urldate = {2023-03-20},
booktitle = {Progresses in Artificial Intelligence and Neural Systems},
pages = {185--197},
publisher = {Springer},
address = {Singapore},
series = {Smart Innovation, Systems and Technologies},
abstract = {Crohn's disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient's quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI examinations of 800 patients from the University of Palermo Policlinico Hospital. For each E-MRI image, a team of radiologists has extracted 20 features associated with CD, calculated a disease activity index and classified patients into three classes (no activity, mild activity and severe activity). The 20 features have been used as the input variables to the SVM classifier, while the activity index has been adopted as the response variable. Different feature reduction techniques have been applied to improve the classifier performance, while a Bayesian optimization technique has been used to find the optimal hyperparameters of the RBF kernel. K-fold cross-validation has been used to enhance the evaluation reliability. The proposed SVM classifier achieved a better performance when compared with other standard classification methods. Experimental results show an accuracy index of 91.45% with an error of 8.55% that outperform the operator-based reference values reported in literature.},
keywords = {Bayesian optimization, Crohn's disease multi-level classification and grading, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, multi-level classifiers, Multiclass support vector machines, Supervised learning},
pubstate = {published},
tppubtype = {incollection}
}
Crohn's disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient's quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI examinations of 800 patients from the University of Palermo Policlinico Hospital. For each E-MRI image, a team of radiologists has extracted 20 features associated with CD, calculated a disease activity index and classified patients into three classes (no activity, mild activity and severe activity). The 20 features have been used as the input variables to the SVM classifier, while the activity index has been adopted as the response variable. Different feature reduction techniques have been applied to improve the classifier performance, while a Bayesian optimization technique has been used to find the optimal hyperparameters of the RBF kernel. K-fold cross-validation has been used to enhance the evaluation reliability. The proposed SVM classifier achieved a better performance when compared with other standard classification methods. Experimental results show an accuracy index of 91.45% with an error of 8.55% that outperform the operator-based reference values reported in literature.
Franchini, Silvia; Vitabile, Salvatore
Geometric Calculus Applications to Medical Imaging: Status and Perspectives Proceedings Article
In: Xambó-Descamps, Sebastià (Ed.): Systems, Patterns and Data Engineering with Geometric Calculi, pp. 31–46, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-74486-1.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics
@inproceedings{franchini_geometric_2021,
title = {Geometric Calculus Applications to Medical Imaging: Status and Perspectives},
author = {Silvia Franchini and Salvatore Vitabile},
editor = {Sebastià Xambó-Descamps},
doi = {10.1007/978-3-030-74486-1_3},
isbn = {978-3-030-74486-1},
year = {2021},
date = {2021-01-01},
booktitle = {Systems, Patterns and Data Engineering with Geometric Calculi},
pages = {31–46},
publisher = {Springer International Publishing},
address = {Cham},
series = {SEMA SIMAI Springer Series},
abstract = {Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.},
keywords = {3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics},
pubstate = {published},
tppubtype = {inproceedings}
}
Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.
Franchini, Silvia; Terranova, Maria Chiara; Re, Giuseppe Lo; Galia, Massimo; Salerno, Sergio; Midiri, Massimo; Vitabile, Salvatore
In: Esposito, Anna; Faundez-Zanuy, Marcos; Morabito, Francesco Carlo; Pasero, Eros (Ed.): Progresses in Artificial Intelligence and Neural Systems, pp. 185–197, Springer, Singapore, 2021, ISBN: 9789811550935.
Abstract | Links | BibTeX | Tags: Bayesian optimization, Crohn’s disease multi-level classification and grading, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, multi-level classifiers, Multiclass support vector machines, Supervised learning
@incollection{franchini_novel_2021,
title = {A Novel System for Multi-level Crohn’s Disease Classification and Grading Based on a Multiclass Support Vector Machine},
author = {Silvia Franchini and Maria Chiara Terranova and Giuseppe Lo Re and Massimo Galia and Sergio Salerno and Massimo Midiri and Salvatore Vitabile},
editor = {Anna Esposito and Marcos Faundez-Zanuy and Francesco Carlo Morabito and Eros Pasero},
url = {https://doi.org/10.1007/978-981-15-5093-5_18},
doi = {10.1007/978-981-15-5093-5_18},
isbn = {9789811550935},
year = {2021},
date = {2021-01-01},
urldate = {2023-03-20},
booktitle = {Progresses in Artificial Intelligence and Neural Systems},
pages = {185–197},
publisher = {Springer},
address = {Singapore},
series = {Smart Innovation, Systems and Technologies},
abstract = {Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI examinations of 800 patients from the University of Palermo Policlinico Hospital. For each E-MRI image, a team of radiologists has extracted 20 features associated with CD, calculated a disease activity index and classified patients into three classes (no activity, mild activity and severe activity). The 20 features have been used as the input variables to the SVM classifier, while the activity index has been adopted as the response variable. Different feature reduction techniques have been applied to improve the classifier performance, while a Bayesian optimization technique has been used to find the optimal hyperparameters of the RBF kernel. K-fold cross-validation has been used to enhance the evaluation reliability. The proposed SVM classifier achieved a better performance when compared with other standard classification methods. Experimental results show an accuracy index of 91.45% with an error of 8.55% that outperform the operator-based reference values reported in literature.},
keywords = {Bayesian optimization, Crohn’s disease multi-level classification and grading, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, multi-level classifiers, Multiclass support vector machines, Supervised learning},
pubstate = {published},
tppubtype = {incollection}
}
Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI examinations of 800 patients from the University of Palermo Policlinico Hospital. For each E-MRI image, a team of radiologists has extracted 20 features associated with CD, calculated a disease activity index and classified patients into three classes (no activity, mild activity and severe activity). The 20 features have been used as the input variables to the SVM classifier, while the activity index has been adopted as the response variable. Different feature reduction techniques have been applied to improve the classifier performance, while a Bayesian optimization technique has been used to find the optimal hyperparameters of the RBF kernel. K-fold cross-validation has been used to enhance the evaluation reliability. The proposed SVM classifier achieved a better performance when compared with other standard classification methods. Experimental results show an accuracy index of 91.45% with an error of 8.55% that outperform the operator-based reference values reported in literature.
2020
Franchini, Silvia; Terranova, Maria Chiara; Re, Giuseppe Lo; Salerno, Sergio; Midiri, Massimo; Vitabile, Salvatore
Evaluation of a Support Vector Machine Based Method for Crohn's Disease Classification Book Section
In: Esposito, Anna; Faundez-Zanuy, Marcos; Morabito, Francesco Carlo; Pasero, Eros (Ed.): Neural Approaches to Dynamics of Signal Exchanges, pp. 313–327, Springer, Singapore, 2020, ISBN: 9789811389504.
Abstract | Links | BibTeX | Tags: Crohn's disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines
@incollection{franchiniEvaluationSupportVector2020,
title = {Evaluation of a Support Vector Machine Based Method for Crohn's Disease Classification},
author = { Silvia Franchini and Maria Chiara Terranova and Giuseppe Lo Re and Sergio Salerno and Massimo Midiri and Salvatore Vitabile},
editor = { Anna Esposito and Marcos {Faundez-Zanuy} and Francesco Carlo Morabito and Eros Pasero},
doi = {10.1007/978-981-13-8950-4_29},
isbn = {9789811389504},
year = {2020},
date = {2020-01-01},
urldate = {2023-03-20},
booktitle = {Neural Approaches to Dynamics of Signal Exchanges},
pages = {313--327},
publisher = {Springer},
address = {Singapore},
series = {Smart Innovation, Systems and Technologies},
abstract = {Crohn's disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.},
keywords = {Crohn's disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines},
pubstate = {published},
tppubtype = {incollection}
}
Crohn's disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.
Franchini, Silvia; Gentile, Antonio; Vassallo, Giorgio; Vitabile, Salvatore
Implementation and Evaluation of Medical Imaging Techniques Based on Conformal Geometric Algebra Journal Article
In: International Journal of Applied Mathematics and Computer Science, vol. 30, no. 3, pp. 415–433, 2020, ISSN: 1641-876X.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Geometric algebra, Medical image registration, Medical image segmentation, Medical Imaging
@article{franchiniImplementationEvaluationMedical2020,
title = {Implementation and Evaluation of Medical Imaging Techniques Based on Conformal Geometric Algebra},
author = { Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.34768/amcs-2020-0031},
issn = {1641-876X},
year = {2020},
date = {2020-01-01},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {30},
number = {3},
pages = {415--433},
abstract = {Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5. textcopyright 2020 Sciendo. All rights reserved.},
keywords = {3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Geometric algebra, Medical image registration, Medical image segmentation, Medical Imaging},
pubstate = {published},
tppubtype = {article}
}
Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5. textcopyright 2020 Sciendo. All rights reserved.
Franchini, Silvia; Terranova, Maria Chiara; Re, Giuseppe Lo; Salerno, Sergio; Midiri, Massimo; Vitabile, Salvatore
Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification Book Section
In: Esposito, Anna; Faundez-Zanuy, Marcos; Morabito, Francesco Carlo; Pasero, Eros (Ed.): Neural Approaches to Dynamics of Signal Exchanges, pp. 313–327, Springer, Singapore, 2020, ISBN: 9789811389504.
Abstract | Links | BibTeX | Tags: Crohn’s disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines
@incollection{franchini_evaluation_2020,
title = {Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification},
author = {Silvia Franchini and Maria Chiara Terranova and Giuseppe Lo Re and Sergio Salerno and Massimo Midiri and Salvatore Vitabile},
editor = {Anna Esposito and Marcos Faundez-Zanuy and Francesco Carlo Morabito and Eros Pasero},
url = {https://doi.org/10.1007/978-981-13-8950-4_29},
doi = {10.1007/978-981-13-8950-4_29},
isbn = {9789811389504},
year = {2020},
date = {2020-01-01},
urldate = {2023-03-20},
booktitle = {Neural Approaches to Dynamics of Signal Exchanges},
pages = {313–327},
publisher = {Springer},
address = {Singapore},
series = {Smart Innovation, Systems and Technologies},
abstract = {Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.},
keywords = {Crohn’s disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines},
pubstate = {published},
tppubtype = {incollection}
}
Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.
Franchini, Silvia; Gentile, Antonio; Vassallo, Giorgio; Vitabile, Salvatore
Implementation and evaluation of medical imaging techniques based on conformal geometric algebra Journal Article
In: International Journal of Applied Mathematics and Computer Science, vol. 30, no. 3, pp. 415–433, 2020, ISSN: 1641-876X.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Geometric algebra, Medical image registration, Medical image segmentation, Medical Imaging
@article{franchini_implementation_2020,
title = {Implementation and evaluation of medical imaging techniques based on conformal geometric algebra},
author = {Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.34768/amcs-2020-0031},
issn = {1641-876X},
year = {2020},
date = {2020-01-01},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {30},
number = {3},
pages = {415–433},
abstract = {Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5. © 2020 Sciendo. All rights reserved.},
keywords = {3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Geometric algebra, Medical image registration, Medical image segmentation, Medical Imaging},
pubstate = {published},
tppubtype = {article}
}
Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5. © 2020 Sciendo. All rights reserved.
2015
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
ConformalALU: A Conformal Geometric Algebra Coprocessor for Medical Image Processing Journal Article
In: IEEE Transactions on Computers, vol. 64, no. 4, pp. 955–970, 2015, ISSN: 0018-9340.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Growing Neural Gas, iterative closest point (ICP), marching spheres, Medical image registration, Medical Imaging, Segmentation, systems-on-programmable-chip, thin-plate spline robust point matching (TPS-RPM), Volume registration
@article{franchiniConformalALUConformalGeometric2015,
title = {ConformalALU: A Conformal Geometric Algebra Coprocessor for Medical Image Processing},
author = { Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/TC.2014.2315652},
issn = {0018-9340},
year = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Computers},
volume = {64},
number = {4},
pages = {955--970},
abstract = {Medical imaging involves important computational geometric problems, such as image segmentation and analysis, shape approximation, three-dimensional (3D) modeling, and registration of volumetric data. In the last few years, Conformal Geometric Algebra (CGA), based on five-dimensional (5D) Clifford Algebra, is emerging as a new paradigm that offers simple and universal operators for the representation and solution of complex geometric problems. However, the widespread use of CGA has been so far hindered by its high dimensionality and computational complexity. This paper proposes a simplified formulation of the conformal geometric operations (reflections, rotations, translations, and uniform scaling) aimed at a parallel hardware implementation. A specialized coprocessing architecture (ConformalALU) that offers direct hardware support to the new CGA operators, is also presented. The ConformalALU has been prototyped as a complete System-on-Programmable-Chip (SoPC) on the Xilinx ML507 FPGA board, containing a Virtex-5 FPGA device. Experimental results show average speedups of one order of magnitude for CGA rotations, translations, and dilations with respect to the geometric algebra software library Gaigen running on the general-purpose PowerPC processor embedded in the target FPGA device. A suite of medical imaging applications, including segmentation, 3D modeling and registration of medical data, has been used as testbench to evaluate the coprocessor effectiveness. textcopyright 2015 IEEE.},
keywords = {3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Growing Neural Gas, iterative closest point (ICP), marching spheres, Medical image registration, Medical Imaging, Segmentation, systems-on-programmable-chip, thin-plate spline robust point matching (TPS-RPM), Volume registration},
pubstate = {published},
tppubtype = {article}
}
Medical imaging involves important computational geometric problems, such as image segmentation and analysis, shape approximation, three-dimensional (3D) modeling, and registration of volumetric data. In the last few years, Conformal Geometric Algebra (CGA), based on five-dimensional (5D) Clifford Algebra, is emerging as a new paradigm that offers simple and universal operators for the representation and solution of complex geometric problems. However, the widespread use of CGA has been so far hindered by its high dimensionality and computational complexity. This paper proposes a simplified formulation of the conformal geometric operations (reflections, rotations, translations, and uniform scaling) aimed at a parallel hardware implementation. A specialized coprocessing architecture (ConformalALU) that offers direct hardware support to the new CGA operators, is also presented. The ConformalALU has been prototyped as a complete System-on-Programmable-Chip (SoPC) on the Xilinx ML507 FPGA board, containing a Virtex-5 FPGA device. Experimental results show average speedups of one order of magnitude for CGA rotations, translations, and dilations with respect to the geometric algebra software library Gaigen running on the general-purpose PowerPC processor embedded in the target FPGA device. A suite of medical imaging applications, including segmentation, 3D modeling and registration of medical data, has been used as testbench to evaluate the coprocessor effectiveness. textcopyright 2015 IEEE.
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
ConformalALU: A conformal geometric algebra coprocessor for medical image processing Journal Article
In: IEEE Transactions on Computers, vol. 64, no. 4, pp. 955–970, 2015, ISSN: 0018-9340.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Growing Neural Gas, iterative closest point (ICP), marching spheres, Medical image registration, Medical Imaging, Segmentation, systems-on-programmable-chip, thin-plate spline robust point matching (TPS-RPM), Volume registration
@article{franchini_conformalalu_2015,
title = {ConformalALU: A conformal geometric algebra coprocessor for medical image processing},
author = {Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/TC.2014.2315652},
issn = {0018-9340},
year = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Computers},
volume = {64},
number = {4},
pages = {955–970},
abstract = {Medical imaging involves important computational geometric problems, such as image segmentation and analysis, shape approximation, three-dimensional (3D) modeling, and registration of volumetric data. In the last few years, Conformal Geometric Algebra (CGA), based on five-dimensional (5D) Clifford Algebra, is emerging as a new paradigm that offers simple and universal operators for the representation and solution of complex geometric problems. However, the widespread use of CGA has been so far hindered by its high dimensionality and computational complexity. This paper proposes a simplified formulation of the conformal geometric operations (reflections, rotations, translations, and uniform scaling) aimed at a parallel hardware implementation. A specialized coprocessing architecture (ConformalALU) that offers direct hardware support to the new CGA operators, is also presented. The ConformalALU has been prototyped as a complete System-on-Programmable-Chip (SoPC) on the Xilinx ML507 FPGA board, containing a Virtex-5 FPGA device. Experimental results show average speedups of one order of magnitude for CGA rotations, translations, and dilations with respect to the geometric algebra software library Gaigen running on the general-purpose PowerPC processor embedded in the target FPGA device. A suite of medical imaging applications, including segmentation, 3D modeling and registration of medical data, has been used as testbench to evaluate the coprocessor effectiveness. © 2015 IEEE.},
keywords = {3D modeling, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Growing Neural Gas, iterative closest point (ICP), marching spheres, Medical image registration, Medical Imaging, Segmentation, systems-on-programmable-chip, thin-plate spline robust point matching (TPS-RPM), Volume registration},
pubstate = {published},
tppubtype = {article}
}
Medical imaging involves important computational geometric problems, such as image segmentation and analysis, shape approximation, three-dimensional (3D) modeling, and registration of volumetric data. In the last few years, Conformal Geometric Algebra (CGA), based on five-dimensional (5D) Clifford Algebra, is emerging as a new paradigm that offers simple and universal operators for the representation and solution of complex geometric problems. However, the widespread use of CGA has been so far hindered by its high dimensionality and computational complexity. This paper proposes a simplified formulation of the conformal geometric operations (reflections, rotations, translations, and uniform scaling) aimed at a parallel hardware implementation. A specialized coprocessing architecture (ConformalALU) that offers direct hardware support to the new CGA operators, is also presented. The ConformalALU has been prototyped as a complete System-on-Programmable-Chip (SoPC) on the Xilinx ML507 FPGA board, containing a Virtex-5 FPGA device. Experimental results show average speedups of one order of magnitude for CGA rotations, translations, and dilations with respect to the geometric algebra software library Gaigen running on the general-purpose PowerPC processor embedded in the target FPGA device. A suite of medical imaging applications, including segmentation, 3D modeling and registration of medical data, has been used as testbench to evaluate the coprocessor effectiveness. © 2015 IEEE.
2013
Franchini, Silvia; Gentile, Antonio; Vassallo, Giorgio; Sorbello, Filippo; Vitabile, Salvatore
A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra Proceedings Article
In: pp. 128–135, 2013, ISBN: 978-0-7695-4992-7.
Abstract | Links | BibTeX | Tags: Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images
@inproceedings{franchiniSpecializedArchitectureColor2013,
title = {A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra},
author = { Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Filippo Sorbello and Salvatore Vitabile},
doi = {10.1109/CISIS.2013.29},
isbn = {978-0-7695-4992-7},
year = {2013},
date = {2013-01-01},
pages = {128--135},
abstract = {Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. textcopyright 2013 IEEE.},
keywords = {Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. textcopyright 2013 IEEE.
Franchini, Silvia; Gentile, Antonio; Vassallo, Giorgio; Sorbello, Filippo; Vitabile, Salvatore
A specialized architecture for color image edge detection based on Clifford algebra Proceedings Article
In: pp. 128–135, 2013, ISBN: 978-0-7695-4992-7.
Abstract | Links | BibTeX | Tags: Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images
@inproceedings{franchini_specialized_2013,
title = {A specialized architecture for color image edge detection based on Clifford algebra},
author = {Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Filippo Sorbello and Salvatore Vitabile},
doi = {10.1109/CISIS.2013.29},
isbn = {978-0-7695-4992-7},
year = {2013},
date = {2013-01-01},
pages = {128–135},
abstract = {Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. © 2013 IEEE.},
keywords = {Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. © 2013 IEEE.
2012
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
A Dual-Core Coprocessor with Native 4D Clifford Algebra Support Proceedings Article
In: pp. 419–422, 2012, ISBN: 978-0-7695-4798-5.
Abstract | Links | BibTeX | Tags: Clifford algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Medical Imaging, multi-core architectures
@inproceedings{franchiniDualcoreCoprocessorNative2012,
title = {A Dual-Core Coprocessor with Native 4D Clifford Algebra Support},
author = { Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/DSD.2012.2},
isbn = {978-0-7695-4798-5},
year = {2012},
date = {2012-01-01},
pages = {419--422},
abstract = {Geometric or Clifford Algebra (CA) is a powerful mathematical tool that is attracting a growing attention in many research fields such as computer graphics, computer vision, robotics and medical imaging for its natural and intuitive way to represent geometric objects and their transformations. This paper introduces the architecture of CliffordCoreDuo, an embedded dual-core coprocessor that offers direct hardware support to four-dimensional (4D) Clifford algebra operations. A prototype implementation on an FPGA board is detailed. Experimental results show a 1.6x average speedup of CliffordCoreDuo in comparison with the baseline mono-core architecture. A potential cycle speedup of about 40x over Gaigen 2, a geometric algebra software library generator for general-purpose processors, is also demonstrated. textcopyright 2012 IEEE.},
keywords = {Clifford algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Medical Imaging, multi-core architectures},
pubstate = {published},
tppubtype = {inproceedings}
}
Geometric or Clifford Algebra (CA) is a powerful mathematical tool that is attracting a growing attention in many research fields such as computer graphics, computer vision, robotics and medical imaging for its natural and intuitive way to represent geometric objects and their transformations. This paper introduces the architecture of CliffordCoreDuo, an embedded dual-core coprocessor that offers direct hardware support to four-dimensional (4D) Clifford algebra operations. A prototype implementation on an FPGA board is detailed. Experimental results show a 1.6x average speedup of CliffordCoreDuo in comparison with the baseline mono-core architecture. A potential cycle speedup of about 40x over Gaigen 2, a geometric algebra software library generator for general-purpose processors, is also demonstrated. textcopyright 2012 IEEE.
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
A dual-core coprocessor with native 4D Clifford algebra support Proceedings Article
In: pp. 419–422, 2012, ISBN: 978-0-7695-4798-5.
Abstract | Links | BibTeX | Tags: Clifford algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Medical Imaging, multi-core architectures
@inproceedings{franchini_dual-core_2012,
title = {A dual-core coprocessor with native 4D Clifford algebra support},
author = {Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/DSD.2012.2},
isbn = {978-0-7695-4798-5},
year = {2012},
date = {2012-01-01},
pages = {419–422},
abstract = {Geometric or Clifford Algebra (CA) is a powerful mathematical tool that is attracting a growing attention in many research fields such as computer graphics, computer vision, robotics and medical imaging for its natural and intuitive way to represent geometric objects and their transformations. This paper introduces the architecture of CliffordCoreDuo, an embedded dual-core coprocessor that offers direct hardware support to four-dimensional (4D) Clifford algebra operations. A prototype implementation on an FPGA board is detailed. Experimental results show a 1.6x average speedup of CliffordCoreDuo in comparison with the baseline mono-core architecture. A potential cycle speedup of about 40x over Gaigen 2, a geometric algebra software library generator for general-purpose processors, is also demonstrated. © 2012 IEEE.},
keywords = {Clifford algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Medical Imaging, multi-core architectures},
pubstate = {published},
tppubtype = {inproceedings}
}
Geometric or Clifford Algebra (CA) is a powerful mathematical tool that is attracting a growing attention in many research fields such as computer graphics, computer vision, robotics and medical imaging for its natural and intuitive way to represent geometric objects and their transformations. This paper introduces the architecture of CliffordCoreDuo, an embedded dual-core coprocessor that offers direct hardware support to four-dimensional (4D) Clifford algebra operations. A prototype implementation on an FPGA board is detailed. Experimental results show a 1.6x average speedup of CliffordCoreDuo in comparison with the baseline mono-core architecture. A potential cycle speedup of about 40x over Gaigen 2, a geometric algebra software library generator for general-purpose processors, is also demonstrated. © 2012 IEEE.
2011
Gallo, Luigi; Placitelli, Alessio Pierluigi; Ciampi, Mario
Controller-Free Exploration of Medical Image Data: Experiencing the Kinect Proceedings Article
In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6, IEEE, Bristol, United Kingdom, 2011, ISBN: 978-1-4577-1189-3.
Abstract | Links | BibTeX | Tags: Healthcare, Kinect, Medical Imaging, Touchless interaction
@inproceedings{galloControllerfreeExplorationMedical2011,
title = {Controller-Free Exploration of Medical Image Data: Experiencing the Kinect},
author = { Luigi Gallo and Alessio Pierluigi Placitelli and Mario Ciampi},
doi = {10.1109/CBMS.2011.5999138},
isbn = {978-1-4577-1189-3},
year = {2011},
date = {2011-06-01},
booktitle = {2011 24th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {1--6},
publisher = {IEEE},
address = {Bristol, United Kingdom},
abstract = {In this paper, an open-source system for a controller-free, highly interactive exploration of medical images is presented. By using a Microsoft Xbox KinectTM as the only input device, the system's user interface allows users to interact at a distance through hand and arm gestures. The paper also details the interaction techniques we have designed specifically for the deviceless exploration of medical imaging data. Since the user interface is touch-free and does not require complex calibration steps, it is suitable for use in operating rooms, where non-sterilizable devices cannot be used.},
keywords = {Healthcare, Kinect, Medical Imaging, Touchless interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, an open-source system for a controller-free, highly interactive exploration of medical images is presented. By using a Microsoft Xbox KinectTM as the only input device, the system's user interface allows users to interact at a distance through hand and arm gestures. The paper also details the interaction techniques we have designed specifically for the deviceless exploration of medical imaging data. Since the user interface is touch-free and does not require complex calibration steps, it is suitable for use in operating rooms, where non-sterilizable devices cannot be used.
Gallo, Luigi; Placitelli, Alessio Pierluigi; Ciampi, Mario
Controller-free exploration of medical image data: experiencing the Kinect Proceedings Article
In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6, IEEE, Bristol, United Kingdom, 2011, ISBN: 978-1-4577-1189-3.
Abstract | Links | BibTeX | Tags: Healthcare, Kinect, Medical Imaging, Touchless interaction
@inproceedings{gallo_controller-free_2011,
title = {Controller-free exploration of medical image data: experiencing the Kinect},
author = {Luigi Gallo and Alessio Pierluigi Placitelli and Mario Ciampi},
doi = {10.1109/CBMS.2011.5999138},
isbn = {978-1-4577-1189-3},
year = {2011},
date = {2011-06-01},
booktitle = {2011 24th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {1–6},
publisher = {IEEE},
address = {Bristol, United Kingdom},
abstract = {In this paper, an open-source system for a controller-free, highly interactive exploration of medical images is presented. By using a Microsoft Xbox KinectTM as the only input device, the system's user interface allows users to interact at a distance through hand and arm gestures. The paper also details the interaction techniques we have designed specifically for the deviceless exploration of medical imaging data. Since the user interface is touch-free and does not require complex calibration steps, it is suitable for use in operating rooms, where non-sterilizable devices cannot be used.},
keywords = {Healthcare, Kinect, Medical Imaging, Touchless interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, an open-source system for a controller-free, highly interactive exploration of medical images is presented. By using a Microsoft Xbox KinectTM as the only input device, the system's user interface allows users to interact at a distance through hand and arm gestures. The paper also details the interaction techniques we have designed specifically for the deviceless exploration of medical imaging data. Since the user interface is touch-free and does not require complex calibration steps, it is suitable for use in operating rooms, where non-sterilizable devices cannot be used.
Pietro, Giuseppe De; Ciampi, Mario; Gallo, Luigi; Minutolo, Aniello
MITO: Medical Imaging Toolkit Miscellaneous
2011.
Abstract | BibTeX | Tags: Healthcare, Medical Imaging, Touchless interaction, User interface
@misc{depietroMITOMedicalImaging2011,
title = {MITO: Medical Imaging Toolkit},
author = { Giuseppe De Pietro and Mario Ciampi and Luigi Gallo and Aniello Minutolo},
year = {2011},
date = {2011-01-01},
abstract = {"MITO - Medical Imaging TOolkit" is an open-source (GNU General Public License version 2.0 - GPLv2), Windows-based software architecture for advanced Medical Imaging. Main characteristics are: DICOM compliant, 2D/3D/S3D visualization, image segmentation and fusion, ROI, advanced 3D user interface. Its latest version, the OR edition, provides a 2D/3D interface for manipulating medical images within sterile environments (e.g., operating rooms), in a touchless way. MITO has been downloaded, so far (February 2016) 35,027 times from 160 countries all around the World.},
keywords = {Healthcare, Medical Imaging, Touchless interaction, User interface},
pubstate = {published},
tppubtype = {misc}
}
"MITO - Medical Imaging TOolkit" is an open-source (GNU General Public License version 2.0 - GPLv2), Windows-based software architecture for advanced Medical Imaging. Main characteristics are: DICOM compliant, 2D/3D/S3D visualization, image segmentation and fusion, ROI, advanced 3D user interface. Its latest version, the OR edition, provides a 2D/3D interface for manipulating medical images within sterile environments (e.g., operating rooms), in a touchless way. MITO has been downloaded, so far (February 2016) 35,027 times from 160 countries all around the World.
Pietro, Giuseppe De; Ciampi, Mario; Gallo, Luigi; Minutolo, Aniello
MITO: Medical Imaging Toolkit Miscellaneous
2011.
Abstract | Links | BibTeX | Tags: Healthcare, Medical Imaging, Touchless interaction, User interface
@misc{de_pietro_mito_2011,
title = {MITO: Medical Imaging Toolkit},
author = {Giuseppe De Pietro and Mario Ciampi and Luigi Gallo and Aniello Minutolo},
url = {https://sourceforge.net/projects/mito/},
year = {2011},
date = {2011-01-01},
abstract = {"MITO - Medical Imaging TOolkit" is an open-source (GNU General Public License version 2.0 - GPLv2), Windows-based software architecture for advanced Medical Imaging. Main characteristics are: DICOM compliant, 2D/3D/S3D visualization, image segmentation and fusion, ROI, advanced 3D user interface. Its latest version, the OR edition, provides a 2D/3D interface for manipulating medical images within sterile environments (e.g., operating rooms), in a touchless way. MITO has been downloaded, so far (February 2016) 35,027 times from 160 countries all around the World.},
keywords = {Healthcare, Medical Imaging, Touchless interaction, User interface},
pubstate = {published},
tppubtype = {misc}
}
"MITO - Medical Imaging TOolkit" is an open-source (GNU General Public License version 2.0 - GPLv2), Windows-based software architecture for advanced Medical Imaging. Main characteristics are: DICOM compliant, 2D/3D/S3D visualization, image segmentation and fusion, ROI, advanced 3D user interface. Its latest version, the OR edition, provides a 2D/3D interface for manipulating medical images within sterile environments (e.g., operating rooms), in a touchless way. MITO has been downloaded, so far (February 2016) 35,027 times from 160 countries all around the World.
2010
Gallo, Luigi; Minutolo, Aniello; Pietro, Giuseppe De
A User Interface for VR-ready 3D Medical Imaging by off-the-Shelf Input Devices Journal Article
In: Computers in Biology and Medicine, vol. 40, no. 3, pp. 350–358, 2010, ISSN: 0010-4825.
Abstract | Links | BibTeX | Tags: Healthcare, Interaction techniques, Medical Imaging, Mouse, Natural User Interfaces, Pointing, Rotation, User study, Virtual Reality, Wiimote
@article{galloUserInterfaceVRready2010,
title = {A User Interface for VR-ready 3D Medical Imaging by off-the-Shelf Input Devices},
author = { Luigi Gallo and Aniello Minutolo and Giuseppe De Pietro},
doi = {10.1016/j.compbiomed.2010.01.006},
issn = {0010-4825},
year = {2010},
date = {2010-01-01},
journal = {Computers in Biology and Medicine},
volume = {40},
number = {3},
pages = {350--358},
abstract = {The distinctiveness of clinical environments demands specific solutions in the design of both usable and practical user interfaces for 3D medical imaging. In this work, a novel user interface to provide a direct interaction in 3D space by off-the-shelf input devices is proposed. The interface, which has been implemented and integrated into an open-source medical image viewer, features a depth-enhanced mouse pointer and a novel rotation technique that uses the object's geometry as the rotation handle. The usability of the proposed approach is evaluated to show its effectiveness for use in professional 3D imaging applications.},
keywords = {Healthcare, Interaction techniques, Medical Imaging, Mouse, Natural User Interfaces, Pointing, Rotation, User study, Virtual Reality, Wiimote},
pubstate = {published},
tppubtype = {article}
}
The distinctiveness of clinical environments demands specific solutions in the design of both usable and practical user interfaces for 3D medical imaging. In this work, a novel user interface to provide a direct interaction in 3D space by off-the-shelf input devices is proposed. The interface, which has been implemented and integrated into an open-source medical image viewer, features a depth-enhanced mouse pointer and a novel rotation technique that uses the object's geometry as the rotation handle. The usability of the proposed approach is evaluated to show its effectiveness for use in professional 3D imaging applications.
Gallo, Luigi; Minutolo, Aniello; Pietro, Giuseppe De
A user interface for VR-ready 3D medical imaging by off-the-shelf input devices Journal Article
In: Computers in Biology and Medicine, vol. 40, no. 3, pp. 350–358, 2010, ISSN: 0010-4825.
Abstract | Links | BibTeX | Tags: Healthcare, Interaction techniques, Medical Imaging, Mouse, Natural User Interfaces, Pointing, Rotation, User study, Virtual Reality, Wiimote
@article{gallo_user_2010,
title = {A user interface for VR-ready 3D medical imaging by off-the-shelf input devices},
author = {Luigi Gallo and Aniello Minutolo and Giuseppe De Pietro},
doi = {10.1016/j.compbiomed.2010.01.006},
issn = {0010-4825},
year = {2010},
date = {2010-01-01},
journal = {Computers in Biology and Medicine},
volume = {40},
number = {3},
pages = {350–358},
abstract = {The distinctiveness of clinical environments demands specific solutions in the design of both usable and practical user interfaces for 3D medical imaging. In this work, a novel user interface to provide a direct interaction in 3D space by off-the-shelf input devices is proposed. The interface, which has been implemented and integrated into an open-source medical image viewer, features a depth-enhanced mouse pointer and a novel rotation technique that uses the object's geometry as the rotation handle. The usability of the proposed approach is evaluated to show its effectiveness for use in professional 3D imaging applications.},
keywords = {Healthcare, Interaction techniques, Medical Imaging, Mouse, Natural User Interfaces, Pointing, Rotation, User study, Virtual Reality, Wiimote},
pubstate = {published},
tppubtype = {article}
}
The distinctiveness of clinical environments demands specific solutions in the design of both usable and practical user interfaces for 3D medical imaging. In this work, a novel user interface to provide a direct interaction in 3D space by off-the-shelf input devices is proposed. The interface, which has been implemented and integrated into an open-source medical image viewer, features a depth-enhanced mouse pointer and a novel rotation technique that uses the object's geometry as the rotation handle. The usability of the proposed approach is evaluated to show its effectiveness for use in professional 3D imaging applications.
2008
Gallo, Luigi; Pietro, Giuseppe De; Coronato, Antonio; Marra, Ivana
Toward a Natural Interface to Virtual Medical Imaging Environments Proceedings Article
In: AVI '08 Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 429–432, ACM New York, NY, USA, Napoli, Italy, 2008, ISBN: 978-1-60558-141-5.
Abstract | Links | BibTeX | Tags: 3D interaction, Medical Imaging, Virtual Reality, VTK, Wireless
@inproceedings{galloNaturalInterfaceVirtual2008,
title = {Toward a Natural Interface to Virtual Medical Imaging Environments},
author = { Luigi Gallo and Giuseppe De Pietro and Antonio Coronato and Ivana Marra},
doi = {10.1145/1385569.1385651},
isbn = {978-1-60558-141-5},
year = {2008},
date = {2008-05-01},
booktitle = {AVI '08 Proceedings of the Working Conference on Advanced Visual Interfaces},
pages = {429--432},
publisher = {ACM New York, NY, USA},
address = {Napoli, Italy},
abstract = {Immersive Virtual Reality environments are suitable to support activities related to medicine and medical practice. The immersive visualization of information-rich 3D objects, coming from patient scanned data, provides clinicians with a clear perception of depth and shapes. However, to benefit from immersive visualization in medical imaging, where inspection and manipulation of volumetric data are fundamental tasks, medical experts have to be able to act in the virtual environment by exploiting their real life abilities. In order to reach this goal, it is necessary to take into account user skills and needs so as to design and implement usable and accessible human-computer interaction interfaces. In this paper we present a natural interface for a semi-immersive virtual environment. Such interface is based on an off-the-shelf handheld wireless device and a speech recognition component, and provides clinicians with intuitive interaction modes for inspecting volumetric medical data.},
keywords = {3D interaction, Medical Imaging, Virtual Reality, VTK, Wireless},
pubstate = {published},
tppubtype = {inproceedings}
}
Immersive Virtual Reality environments are suitable to support activities related to medicine and medical practice. The immersive visualization of information-rich 3D objects, coming from patient scanned data, provides clinicians with a clear perception of depth and shapes. However, to benefit from immersive visualization in medical imaging, where inspection and manipulation of volumetric data are fundamental tasks, medical experts have to be able to act in the virtual environment by exploiting their real life abilities. In order to reach this goal, it is necessary to take into account user skills and needs so as to design and implement usable and accessible human-computer interaction interfaces. In this paper we present a natural interface for a semi-immersive virtual environment. Such interface is based on an off-the-shelf handheld wireless device and a speech recognition component, and provides clinicians with intuitive interaction modes for inspecting volumetric medical data.
Gallo, Luigi; Pietro, Giuseppe De; Coronato, Antonio; Marra, Ivana
Toward a Natural Interface to Virtual Medical Imaging Environments Proceedings Article
In: AVI '08 Proceedings of the working conference on Advanced visual interfaces, pp. 429–432, ACM New York, NY, USA, Napoli, Italy, 2008, ISBN: 978-1-60558-141-5.
Abstract | Links | BibTeX | Tags: 3D interaction, Medical Imaging, Virtual Reality, VTK, Wireless
@inproceedings{gallo_toward_2008,
title = {Toward a Natural Interface to Virtual Medical Imaging Environments},
author = {Luigi Gallo and Giuseppe De Pietro and Antonio Coronato and Ivana Marra},
doi = {10.1145/1385569.1385651},
isbn = {978-1-60558-141-5},
year = {2008},
date = {2008-05-01},
booktitle = {AVI '08 Proceedings of the working conference on Advanced visual interfaces},
pages = {429–432},
publisher = {ACM New York, NY, USA},
address = {Napoli, Italy},
abstract = {Immersive Virtual Reality environments are suitable to support activities related to medicine and medical practice. The immersive visualization of information-rich 3D objects, coming from patient scanned data, provides clinicians with a clear perception of depth and shapes. However, to benefit from immersive visualization in medical imaging, where inspection and manipulation of volumetric data are fundamental tasks, medical experts have to be able to act in the virtual environment by exploiting their real life abilities. In order to reach this goal, it is necessary to take into account user skills and needs so as to design and implement usable and accessible human-computer interaction interfaces. In this paper we present a natural interface for a semi-immersive virtual environment. Such interface is based on an off-the-shelf handheld wireless device and a speech recognition component, and provides clinicians with intuitive interaction modes for inspecting volumetric medical data.},
keywords = {3D interaction, Medical Imaging, Virtual Reality, VTK, Wireless},
pubstate = {published},
tppubtype = {inproceedings}
}
Immersive Virtual Reality environments are suitable to support activities related to medicine and medical practice. The immersive visualization of information-rich 3D objects, coming from patient scanned data, provides clinicians with a clear perception of depth and shapes. However, to benefit from immersive visualization in medical imaging, where inspection and manipulation of volumetric data are fundamental tasks, medical experts have to be able to act in the virtual environment by exploiting their real life abilities. In order to reach this goal, it is necessary to take into account user skills and needs so as to design and implement usable and accessible human-computer interaction interfaces. In this paper we present a natural interface for a semi-immersive virtual environment. Such interface is based on an off-the-shelf handheld wireless device and a speech recognition component, and provides clinicians with intuitive interaction modes for inspecting volumetric medical data.