Automated Detection of Architectural Detection in Mammograms Using Template Matching
International Journal of Biomedical Science and Engineering
Volume 2, Issue 1, February 2014, Pages: 1-6
Accepted: Apr. 15, 2014;
Published: Apr. 30, 2014
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O'tega A. Ejofodomi, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Edikan Nse Gideon, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Gbenga Olalekan Oladipo, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Etse Rosemary Oshomah, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Breast Cancer is one of the leading causes of death, and its early detection increases the survival rate and treatment options available to patients. Computer-Aided-Detection (CAD) systems have been developed to assist radiologists with the task of locating cancer in mammograms. Unfortunately, these CAD systems have demonstrated less than 50% efficiency in detecting Architectural Distortions (AD), which is a sign of breast cancer. This paper presents a method of detecting AD with better sensitivity results. Forty mammograms containing AD were obtained from the Digital Database for Screening Mammography (DDSM). Each mammogram was preprocessed using breast segmentation techniques to extract the breast region from the mammogram. The mammograms were enhanced using contrast-limited adaptive histogram equalization (CLAHE). Next, the enhanced mammograms were filtered with a bank of 180 Gabor filters to extract the texture orientation from the images. Based on the fact that ADs contain spicules radiating in all direction, AD templates were designed in MATLAB. These templates were cross-correlated with the Gabor filtered mammograms to obtain ROIs that were most likely to contain ADs. The developed algorithm is intended to assist the radiologist by flagging regions likely to contain AD, prompting the radiologist to take a closer look at those regions. The current algorithm developed in MATLAB automatically flags seven suspicious blocks of 25 by 25 pixels per image, and demonstrated a sensitivity of 87.5% with a False Positive per Image (FPI) of 5.2. Future work will focus on the reduction of FPI.
O'tega A. Ejofodomi,
Edikan Nse Gideon,
Gbenga Olalekan Oladipo,
Etse Rosemary Oshomah,
Automated Detection of Architectural Detection in Mammograms Using Template Matching, International Journal of Biomedical Science and Engineering.
Vol. 2, No. 1,
2014, pp. 1-6.
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