An adaptive region growing algorithm for breast masses in mammograms

Abstract

This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors. An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper. In order to accommodate different situations of masses, the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy. 106 benign and 110 malignant tumors were included in this study. We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns. Then the segmented results were evaluated by the classification accuracy. 42 features including age, intensity, shape and texture were extracted from each segmented mass and support vector machine (SVM) was used as a classifier. The classification accuracy was evaluated using the area (A_z) under the receiver operating characteristic (ROC) curve. It was found that the maximum likelihood analysis achieved an A_z value of 0.835, the maximum gradient analysis got an A_z value of 0.932 and the hybrid assessment function performed the best classification result where the value of A_z was 0.948. In addition, compared with traditional region growing algorithm, our proposed algorithm is more adaptive and provides a better performance for future works.

Keywords

mass lesion segmentation / adaptive region growing algorithm / maximum likelihood analysis / information entropy / support vector machine (SVM)