Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle

by   R. Subash Chandra Boss, et al.

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.



page 2

page 3

page 4

page 5

page 6


Automatic Application Level Set Approach in Detection Calcifications in Mammographic Image

Breast cancer is considered as one of a major health problem that consti...

Pectoral Muscles Suppression in Digital Mammograms using Hybridization of Soft Computing Methods

Breast region segmentation is an essential prerequisite in computerized ...

A CNN-based methodology for breast cancer diagnosis using thermal images

Micro Abstract: A recent study from GLOBOCAN disclosed that during 2018 ...

Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions

Background While breast imaging such as full-field digital mammography a...

Descriptive analysis of computational methods for automating mammograms with practical applications

Mammography is a vital screening technique for early revealing and ident...

On segmentation of pectoralis muscle in digital mammograms by means of deep learning

Computer-aided diagnosis (CAD) has long become an integral part of radio...

Computer Aided Detection of Deep Inferior Epigastric Perforators in Computed Tomography Angiography scans

The deep inferior epigastric artery perforator (DIEAP) flap is the most ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.