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Updated: May 15, 2026

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

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

Published on: August 30, 2013

Breast mass contour segmentation algorithm in digital mammograms.

Tolga Berber¹, Adil Alpkocak, Pinar Balci

¹Dokuz Eylul University, Department of Computer Engineering, Graduate School of Natural and Applied Sciences, Izmir, Turkey. tberber@cs.deu.edu.tr

Computer Methods and Programs in Biomedicine

|January 1, 2013

Summary

This summary is machine-generated.

This study introduces a novel breast mass contour segmentation algorithm for mammograms. The new method improves contour accuracy, enhancing computer-aided diagnosis (CAD) for radiologists.

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Biomedical Engineering

Background:

Accurate breast mass contour detection is crucial for mass shape recognition in mammography.
Existing computer-aided diagnosis (CAD) systems require precise mass contours for reliable interpretation.

Purpose of the Study:

To develop and evaluate a novel breast mass contour segmentation algorithm.
To enhance the accuracy of mass contour detection in mammograms for improved CAD systems.

Main Methods:

A new segmentation algorithm based on the seed region growing algorithm was developed.
The algorithm adaptively adjusts threshold values for precise contour extraction.
The method was evaluated on a dataset of 260 manually annotated breast masses.

Main Results:

The proposed algorithm demonstrated superior performance compared to existing methods.
Evaluation metrics included specificity, sensitivity, balanced accuracy, and error distances (Yassnoff, Hausdorff).

Conclusions:

The novel breast mass contour segmentation algorithm significantly improves contour accuracy.
This advancement has the potential to enhance the reliability of computer-aided diagnosis in mammography.