Publications

@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}

}

@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}

}