Detecting Architectural Distortion in Mammograms Using a Gabor Filtered Probability Map Algorithm

Abstract : Breast Cancer is a disease that is prevalent in many countries. Computer-Aided detection (CAD) systems have been developed to assist radiologists in detecting breast cancer. This paper discusses an algorithm for architectural distortion (AD) detection with a better sensitivity than the current CAD systems.19 images containing ADs were preprocessed with a median filter and Gabor filters to extract texture information. AD probability maps were generated using a maximum amplitude map and histogram analysis on the orientation map of the Gabor filter response. AD maps were analyzed to select ROIs as potential AD sites.AD map analysis yielded a sensitivity of 79% (15 out of 19 cases of AD were detected) with a false positive per image (FPI) of 18. Future work involves the development of a second stage in the algorithm to reduce the FPI value and application of the algorithm to a different set of database images.
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O’tega Ejofodomi, Michael Olawuyi, Don Onyishi, Godswill Ofualagba. Detecting Architectural Distortion in Mammograms Using a Gabor Filtered Probability Map Algorithm. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. pp.328-335, ⟨10.1007/978-3-642-41142-7_34⟩. ⟨hal-01459674⟩

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