Segmentation of Ultrasound Breast Images using Vector Neighborhood with Vector Sequencing on KMCG and augmented KMCG algorithms

Abstract

B mode ultrasound (US) imaging is popular and important modality to examine the range of clinical problems and also used as complimentary to the mammogram imaging to detect and diagnose the nature breast tumor. To understand the nature (benign or malignant) of the tumor most of the radiologists focus on shape and boundary. Therefore boundary is as important characteristic of the tumor along with the shape. Tracing the contour manually is a time consuming and tedious task. Automated and efficient segmentation method also helps radiologists to understand and observe the volume of a tumor (growth or shrinkage). Inherent artifact present in US images, such as speckle, attenuation and shadows are major hurdles in achieving proper segmentation. Along with these artifacts, inhomogeneous texture present in the region of interest is also a major concern. Most of the algorithms studies in the literature include noise removal technique as a preprocessing step. Here in this paper, we are eliminating this step and directly handling the images with high degree of noise. VQ based clustering technique is proposed for US image segmentation with KMCG and augmented KMCG codebook generation algorithms. Using this algorithm images are divided in to clusters, further these clusters are merged sequentially. A novel technique of sequential cluster merging with vector sequencing has been used. We have also proposed a technique to find out the region of interest from the selected cluster with seed vector acquisition. Results obtained by our method are compared with our earlier method and Marker Controlled Watershed transform. With the opinion of the expert radiologist, we found that our method gives better results.