Histogram-based approach for mass segmentation in mammograms
Zobia Suhail, Reyer Zwiggelaar · 2020
Segmentation of abnormalities in mammographic images has been a challenge due to poor contrast between the abnormality and surrounding area. In general Computer Aided Diagnostic (CAD) systems are composed of three major step: i.e segmentation followed by feature extraction and classification. The ultimate results of CAD systems are strongly influenced by the results of segmentation, as poor segmentation leads to incorrect features being extracted and ultimately incorrect classification. In this paper we propose a novel method for the segmentation of masses in mammographic images by using a simple method based on the information provided in histograms. After applying some preprocessing on the mammographic images in order to smooth the image histograms, optimal threshold values were obtained for each mammogram which can deal with uni-modal and bi-modal histograms. Segmentation results show an improved area for the mammogram masses, representing the true shapes of the abnormalities. We tested our algorithm on 233 benign and 233 malignant abnormalities. Results are in line with state of the art methods.