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.
Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.328-335, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_34〉
https://hal.inria.fr/hal-01459674
Contributeur : Hal Ifip
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Soumis le : mardi 7 février 2017 - 13:14:53
Dernière modification le : vendredi 1 décembre 2017 - 01:16:33
Document(s) archivé(s) le : lundi 8 mai 2017 - 14:20:37
O’tega Ejofodomi, Michael Olawuyi, Don Onyishi, Godswill Ofualagba. Detecting Architectural Distortion in Mammograms Using a Gabor Filtered Probability Map Algorithm. Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.328-335, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_34〉. 〈hal-01459674〉
