Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle

TL;DR

This paper presents an automated histogram-based method for extracting breast regions and removing pectoral muscle in mammogram images to improve breast cancer detection accuracy.

Contribution

It introduces a novel 8-neighborhood connected component labeling technique for precise breast region segmentation and pectoral muscle removal.

Findings

Higher accuracy in breast region identification

Effective removal of pectoral muscle

Improved potential for CAD system performance

Abstract

Currently Mammography is a most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast region segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the removal of pectoral muscle are essential pre-processing steps in Computer Aided Diagnosis (CAD) system for the diagnosis of breast cancer. Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram. The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram image pre-processing. The presence of pectoral muscle in mammograms may disturb or influence the detection of breast cancer as the pectoral muscle and mammographic parenchymas appear similar. The goal of breast region extraction is reducing the image size without losing anatomic information, it improve the accuracy of the overall CAD system. The main objective of this study is to propose an automated method to identify the pectoral muscle in Medio-Lateral Oblique (MLO) view mammograms. In this paper, we proposed histogram based 8-neighborhood connected component labelling method for breast region extraction and removal of pectoral muscle. The proposed method is evaluated by using the mean values of accuracy and error. The comparative analysis shows that the proposed method identifies the breast region more accurately.