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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Ejofodomi, O., Olawuyi, M., Onyishi, D.U., Ofualagba, G. (2013). Detecting Architectural Distortion in Mammograms Using a Gabor Filtered Probability Map Algorithm. In: Papadopoulos, H., Andreou, A.S., Iliadis, L., Maglogiannis, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2013. IFIP Advances in Information and Communication Technology, vol 412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41142-7_34
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DOI: https://doi.org/10.1007/978-3-642-41142-7_34
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