A Machine Learning Approach for Computer-Aided Detection of Cerebral Microbleed Using High-order Shape Features

Abstract : This paper presents a novel machine learning approach for computer-aided detection of microbleeds in SWI. The major contributions are: identifying microbleed extent in order to extract proper cubic regions-of-interest (ROI) containing the structure, (2) extracting a set of robust 3- dimensional (3D) Radon- and Hessian-based shape descriptors within the ROIs as well as 2D Radon features computed on intensity-projection images of the corresponding ROIs, and (3) incorporating a cascade of random forests (RF) classifiers to iteratively reduce false detection rates while maintaining a high sensitivity.
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Communication dans un congrès
ISMRM 2014, May 2014, Milan, Italy. pp.1956, 2014
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Amir Fazlollahi, Fabrice Meriaudeau, Luca Giancardo, Christopher Rowe, Victor L Villemagne, et al.. A Machine Learning Approach for Computer-Aided Detection of Cerebral Microbleed Using High-order Shape Features. ISMRM 2014, May 2014, Milan, Italy. pp.1956, 2014. <hal-00989923>

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