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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Automatic detection of pectoral muscle using average gradient and shape based feature.

Jayasree Chakraborty¹, Sudipta Mukhopadhyay, Veenu Singla

¹Computer Vision Lab, Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, West Bengal, India. jayasree2@gmail.com

Journal of Digital Imaging

|October 19, 2011

Summary

This study introduces a novel method for accurately detecting pectoral muscles in mammograms, improving breast cancer detection by segmenting these similar-looking tissues. The approach offers simplicity and high accuracy across various imaging types.

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Biomedical Engineering

Background:

Pectoral muscle in mammograms can obscure breast cancer detection due to visual similarity with abnormal tissues.
Accurate segmentation of pectoral muscles is crucial for reliable breast cancer diagnosis.

Purpose of the Study:

To propose a novel, simple, and accurate method for pectoral muscle detection in mammograms.
To enhance the accuracy of breast cancer detection by effectively segmenting pectoral muscles.

Main Methods:

A novel approach utilizing average gradient- and shape-based features for pectoral muscle detection.
Approximation of pectoral muscle boundary as a straight line, refined into a smooth curve.
Generation of a segmentation mask to represent the enclosed pectoral muscle region.

Main Results:

The method demonstrated simplicity and accuracy on 200 mammographic images (mini-MIAS, DR, CR).
Achieved low average false positive (FP) and false negative (FN) pixel percentages (e.g., 4.22% FP, 3.93% FN for mini-MIAS).
Outperformed two established pectoral muscle detection techniques in comparative analysis.

Conclusions:

The proposed method effectively segments pectoral muscles in mammograms.
This technique can improve the reliability and accuracy of breast cancer detection systems.
The approach offers a valuable tool for radiologists and diagnostic systems.