Novel method of segmenting breast lesion in ultrasound images using grouping bandlets

Abstract

Segmentation of medical ultrasound images is one of the most important functional components of medical ultrasonic instruments for computed aided diagnosis, such as breast lesion early detection and measurement. However, the segmentation of breast lesions from ultrasound images is still a challenging task due to the variance in shape of the lesions and interference from speckle noise. In this paper, a novel approach using tight frames of grouping bandlet(TFGB) and improved stochastic neighbor embedding algorithm(ISNE) is adopted for the segmentation of breast lesions. Novelty in this paper includes that grouplet transform is successively applied and adapted for image segmentation for the first time whereas all of previous works were focused on image inpainting, texture synthesis and image denoising. Moreover, an effective stopping criterion is proposed to improve the dimension reduction technique-stochastic neighbor embedding (SNE) in this paper. Applications to clinical ultrasound images with fibroadenoma and fibrocystic breast lesions contaminated by speckle noise are performed, respectively. Experimental results show that compared with the state-of-the-art approach to the segmentation of breast lesions in ultrasound images, the proposed approach demonstrates robustness to speckle noise and superior performance on the effectiveness.