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.
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Franchini, S., Vitabile, S. (2021). Geometric Calculus Applications to Medical Imaging: Status and Perspectives. In: Xambó-Descamps, S. (eds) Systems, Patterns and Data Engineering with Geometric Calculi. SEMA SIMAI Springer Series(), vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-74486-1_3
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