Authors:
Quynh Tran
1
;
Tina Santner
2
and
Antonio Rodríguez-Sánchez
1
Affiliations:
1
Department of Computer Science, University of Innsbruck, Innsbruck, Austria
;
2
Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria
Keyword(s):
Mammogram, Posterior Nipple Line Detection, Computer Vision.
Abstract:
Breast cancer is the most commonly diagnosed cancer in female patients. Detecting early signs of malignity by undergoing breast screening is therefore of great importance. For a reliable diagnosis, high-quality exami-nated mammograms are essential since poor breast positioning can cause cancers to be missed, which is why mammograms are subject to strict evaluation criteria. One such criterion is the posterior (or pectoralis) nipple line (PNL). We present a method for computing the PNL length, which consisted of the following steps: Pectoral Muscle Detection, Nipple Detection, and final PNL Computation. A multidirectional Gabor filter allowed us to detect the pectoral muscle. For detecting the nipple we made use of the geometric properties of the breast, applied watershed segmentation and Hough Circle Transform. Using both landmarks (pectoral muscle and nipple), the PNL length could be computed. We evaluated 100 mammogram images provided by the Medical University of Innsbruck. The com
puted PNL length was compared with the real PNL length, which was measured by an expert. Our methodology achieved an absolute mean error of just 6.39 mm.
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