Two graph theory based methods for identifying the pectoral muscle in mammograms

Authors:
Fei Ma;Mariusz Bajger;John P. Slavotinek;Murk J. Bottema
Affiliations:
School of Informatics and Engineering, Flinders University, P.O. Box 2100, Adelaide, SA 5001, Australia;School of Informatics and Engineering, Flinders University, P.O. Box 2100, Adelaide, SA 5001, Australia;Department of Medical Imaging, Flinders Medical Centre, Bedford Park, SA 5042, Australia;School of Informatics and Engineering, Flinders University, P.O. Box 2100, Adelaide, SA 5001, Australia
Venue:
Pattern Recognition
Year:
2007

Citing 8
Cited 3

Introduction to algorithms

Introduction to algorithms
Hierarchical Image Analysis Using Irregular Tessellations

IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast algorithm for active contours and curvature estimation

CVGIP: Image Understanding
The adaptive pyramid: a framework for 2D image analysis

CVGIP: Image Understanding
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation

International Journal of Computer Vision
A new graph-theoretic approach to clustering and segmentation

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Temporal Analysis of Mammograms Based on Graph Matching

IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation

Pattern Recognition
Pectoral muscle segmentation: A review

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.01

Visualization

Abstract

Two image segmentation methods based on graph theory are used in conjunction with active contours to segment the pectoral muscle in screening mammograms. One method is based on adaptive pyramids (AP) and the other is based on minimum spanning trees (MST). The algorithms are tested on a public data set of mammograms and results are compared with previously reported methods. In 80% of the images, the boundary of the segmented regions has average error less than 2mm. In 82 of 84 images, the boundary of the pectoral muscle found by the AP algorithm has average error less than 5mm.