Automated Breast Lesion Segmentation in Ultrasound Images

TL;DR

This paper presents an automated method for segmenting breast lesions in ultrasound images by removing noise, segmenting regions with normalized cuts, and classifying lesions with K-means, achieving accurate results compared to ground truth.

Contribution

It introduces a three-stage segmentation approach combining noise removal, normalized cuts, and K-means classification for improved lesion detection in ultrasound images.

Findings

Effective noise reduction enhances segmentation accuracy.

Normalized cuts accurately identify regions of interest.

K-means classification reliably locates lesions.

Abstract

The main objective of this project is to segment different breast ultrasound images to find out lesion area by discarding the low contrast regions as well as the inherent speckle noise. The proposed method consists of three stages (removing noise, segmentation, classification) in order to extract the correct lesion. We used normalized cuts approach to segment ultrasound images into regions of interest where we can possibly finds the lesion, and then K-means classifier is applied to decide finally the location of the lesion. For every original image, an annotated ground-truth image is given to perform comparison with the obtained experimental results, providing accurate evaluation measures.