A Review of Segmentation Algorithms Applied to B-Mode Breast Ultrasound Images: A Characterization Approach

Abstract

Ultrasound imaging modality is used prominently for breast cancer screening and diagnosis because of its safety, portability, ease of use and low cost. Over the years, computer-assisted algorithms have been used to aid the radiologists for interpreting the ultrasound images. The presence of speckle adversely affects the ultrasound image quality because of which accurate segmentation of tumors has become a challenging task. In the present work, various machine learning (ML) and deep learning (DL) based approaches designed for segmenting breast ultrasound images have been reviewed over the past two decades using a characterization approach in terms of (a) datasets used, (b) pre-processing methods, (c) augmentation methods, (d) segmentation methods and (e) evaluation metrics used for the segmentation algorithms along with their brainstorming diagrams. The review presents the achievements made till date in the design of ML and DL based segmentation methods applied to breast ultrasound images and also highlights the directions in which the future research could be carried out.