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

arXiv: https://arxiv.org/abs/2210.10975 · HTML: https://mlsanity.com/paper/2210.10975/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [BUSIS: A Benchmark for Breast Ultrasound Image Segmentation](https://mlsanity.com/paper/1801.03182/) (2018)
- [Gastrointestinal Disorder Detection with a Transformer Based Approach](https://mlsanity.com/paper/2210.03168/) (2022)
- [PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images](https://mlsanity.com/paper/2210.00407/) (2022)
- [Imrpoving Strain Estimation in Breast Ultrasound Images Using Novel 1.5D Approach (Simulation and In-vivo results](https://mlsanity.com/paper/2210.06677/) (2022)

## Abstract (arXiv)

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.