A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Abstract

Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions. The proposed method provides fully-automatic segmentation of the suspicious regions. This method is based on determining an adaptive multi-threshold value by using three different techniques together. These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications. In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss. The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature. The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient. According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.