AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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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

2020

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}

}