Title
Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression
Abstract
In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases—including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75 % of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.
Year
DOI
Venue
2016
10.1007/s10278-015-9813-5
Journal of Digital Imaging
Keywords
Field
DocType
Computer aided diagnosis,Hough transform,Euclidean distance regression,Mammograms,Pectoral muscle detection,Texture gradient
CAD,Computer vision,Line (geometry),Polynomial,Computer science,Segmentation,Computed radiography,Euclidean distance,Computer-aided diagnosis,Hough transform,Artificial intelligence
Journal
Volume
Issue
ISSN
29
1
0897-1889
Citations 
PageRank 
References 
1
0.36
10
Authors
3
Name
Order
Citations
PageRank
Vibha Bafna Bora121.04
A. G. Kothari2289.96
Avinash G. Keskar3127.41