Variational model with image denoising fitting term for boundary extraction of breast ultrasound images

Abstract

A variational model was used to extract or segment the breast ultrasound (BUS) image boundary in order to find a closed curve line of the abnormality region for further diagnosis. A recent selective variational model, termed the Convex Distance Selective Segmentation (CDSS) model, is effective at segmenting a specific image object. However, the CDSS model has difficulty segmenting noisy images. Unavoidable noise in BUS pictures leads to poor segmentation, as is widely recognized. The objective of this work is to propose a reformulation of the Convex Distance Selective Segmentation (CDSS) model for the purpose of segmenting BUS pictures. Consideration of four distinct image Denoising algorithms—Gaussian filter, Median filter, Wiener filter, and Rudin-Osher-Fatemi (ROF) algorithm—as the new fitting terms in the CDSS model leads to four variants of modified CDSS models called Modified CDSS based on Gaussian filter (MCDSS_G), Modified CDSS based on Median filter (MCDSS_M), Modified CDSS based on Wiener filter (MCDSS_W) and Modified CDSS based on ROF (MCDSS_ROF). To solve the modified models, we first derived the associate Euler-Lagrange equation and solved it in Matrix Laboratory (MATLAB) software. Experiments demonstrated that the proposed MCDSS_ROF model based on the ROF denoising algorithm provided the highest average of Peak-Signal-To-Noise-Ratio (PSNR), Dice, and Jaccard Similarity Coefficients, indicating the highest denoising quality and segmentation accuracy in comparison to other models.