Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution

Abstract

Ultrasound imaging is most popular technique used for breast cancer screening. Lesion segmentation is challenging step in characterization of breast ultrasound (US) based Computer Aided Diagnosis (CAD) systems due to presence of speckle noise, shadowing effect etc. The aim of this study is to develop an automatic lesion segmentation technique in breast US with high accuracy even in presence of noises, artifacts and multiple lesions. This article presents a novel clustering method called Multi-scale Gaussian Kernel induced Fuzzy C-means (MsGKFCM) for segmentation of lesions in automatically extracted Region of Interest (ROI) in US to delimit the border of the mass. Further, a hybrid approach using MsGKFCM and Multi-scale Vector Field Convolution (MsVFC) is proposed to obtain an accurate lesion margin in breast US images. Initially, the images are filtered using speckle reducing anisotropic diffusion (SRAD) technique. Subsequently, MsGKFCM is applied on filtered images to segment the mass and detect an appropriate cluster center. The detected cluster center is further used by MsVFC to determine the accurate lesion margin. The proposed technique is evaluated on 127 US images using measures such as Jaccard Index, Dice similarity, Shape similarity, Hausdroff difference, Area difference, Accuracy, F-measure and analysis of variance (ANOVA) test. The empirical results suggest that the proposed approach can be used as an expert system to assist medical professionals by providing objective evidences in breast lesion detection. Results obtained are so far looking promising and effective in comparison to state-of-the-art algorithms.