Computer Aided Segmentation of Breast Ultrasound Images Using Scale Invariant Feature Transform (SIFT) and Bag Of Features

Abstract

Automatic and precise segmentation of breast ultrasound (BUS) image is a challenging task. The proposed method successfully implemented a segmentation algorithm by region growing from a seed point based on texture features generated by Gray Level Co-occurrence Matrix (GLCM). The seed points generated by canny edge detection and wavelet modulus maxima methods are refined by Support Vector Machine (SVM) trained by Scale Invariant Feature Transform (SIFT). The segmented images are compared with ground truth images and True Positive Rate (TPR) of 90.1% and average SI (Similarity Index) of 0.85 demonstrates that the proposed method can segment the tumor regions efficiently and accurately.