Computer-aided diagnosis of diagnostically challenging lesions in breast MRI: a comparison between a radiomics and a feature-selective approach

Sebastian Hoffmann; Marc Lobbes; Ivo Houben; Katja Pinker-Domenig; Georg Wengert; Bernhard Burgeth; Uwe Meyer-Bäse; Guillaume Lemaitre; Anke Meyer-Baese

doi:10.1117/12.2228994

12 July 2016 Computer-aided diagnosis of diagnostically challenging lesions in breast MRI: a comparison between a radiomics and a feature-selective approach

Sebastian Hoffmann, Marc Lobbes, Ivo Houben, Katja Pinker-Domenig, Georg Wengert, Bernhard Burgeth, Uwe Meyer-Bäse, Guillaume Lemaitre, Anke Meyer-Baese

Author Affiliations +

Proceedings Volume 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016; 98710H (2016) https://doi.org/10.1117/12.2228994
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

Diagnostically challenging lesions pose a challenge both for the radiological reading and also for current CAD systems. They are not well-defined in both morphology (geometric shape) and kinetics (temporal enhancement) and pose a problem to lesion detection and classification. Their strong phenotypic differences can be visualized by MRI. Radiomics represents a novel approach to achieve a detailed quantification of the tumour phenotypes by analyzing a large number of image descriptors. In this paper, we apply a quantitative radiomics approach based on shape, texture and kinetics tumor features and evaluate it in comparison to a reduced-order feature approach in a computer-aided diagnosis system applied to diagnostically challenging lesions.

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Sebastian Hoffmann, Marc Lobbes, Ivo Houben, Katja Pinker-Domenig, Georg Wengert, Bernhard Burgeth, Uwe Meyer-Bäse, Guillaume Lemaitre, and Anke Meyer-Baese "Computer-aided diagnosis of diagnostically challenging lesions in breast MRI: a comparison between a radiomics and a feature-selective approach", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710H (12 July 2016); https://doi.org/10.1117/12.2228994

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