Improving Segmentation of Breast Ultrasound Images: Semi Automatic Two Pointers Histogram Splitting Technique

TL;DR

This paper introduces a semi-automatic histogram splitting method with two pointers to enhance edge-map quality and segmentation accuracy in noisy breast ultrasound images, reducing edge pixels and improving homogeneity.

Contribution

A novel semi-automatic histogram splitting technique using two pointers that improves edge-map quality and segmentation in breast ultrasound images.

Findings

Enhanced edge-map quality leads to better segmentation accuracy.

Reduced edge-pixels to area ratio improves homogeneity.

Method outperforms traditional segmentation with same initialization.

Abstract

Automatically segmenting lesion area in breast ultrasound (BUS) images is a challenging one due to its noise, speckle and artifacts. Edge-map of BUS images also does not help because in most cases the edge-map gives no information whatsoever. Almost all segmentation technique takes the edge-map of the image as its first step, though there are a few algorithms that try to avoid edge-maps as well. Improving the edge-map of breast ultrasound images theoretically improves the chances of automatic segmentation to be more precise. In this paper, we propose a semi-automatic technique of histogram splitting using two pointers. Here the user only has to select two initially guessed points denoting a circle on the region of interest (ROI). The method will automatically study the internal histogram and split it using two pointers. The output BUS image has improved edge-map and ultimately the segmentation on it is better compared to regular segmentation using same algorithm and same initialization. Also, we further processed the edge-map to have less edge-pixels to area ratio, improving the homogeneity and the chances of easy segmentation in the future.