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A statistical approach for breast density segmentation.

Arnau Oliver¹, Xavier Lladó, Elsa Pérez

¹Department of Computer Architecture and Technology, IIiA-IdIBGi, University of Girona, Campus Montilivi, Ed. P-IV, 17071 Girona, Spain. aoliver@eia.udg.edu

Journal of Digital Imaging

|June 10, 2009

Summary

This summary is machine-generated.

This study presents a novel method for automatically assessing breast density by segmenting fatty and dense tissues. The approach accurately identifies dense breasts, aiding in breast cancer risk evaluation.

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Area of Science:

Medical Imaging
Radiology
Computer-Aided Diagnosis

Background:

Increased breast density is a significant risk factor for developing breast cancer.
Accurate breast density assessment is crucial for mammography interpretation and risk stratification.

Purpose of the Study:

To develop and validate an automated approach for evaluating breast density.
To segment breast parenchyma into fatty and dense tissue classes.

Main Methods:

Utilizing statistical analysis of pixel neighborhoods to model fatty and dense breast tissues.
Implementing a segmentation approach that considers spatial information to form connected density clusters.
Validating the method on two distinct mammogram databases: MIAS and a full-field digital mammography database with radiologist annotations.

Main Results:

The proposed approach demonstrated robust performance in quantitative and qualitative evaluations.
The method successfully segmented breast parenchyma into fatty and dense tissue types.
Accurate detection of dense breasts was achieved.

Conclusions:

The developed automated approach effectively evaluates breast density by segmenting parenchyma.
This method shows promise for improving the accuracy and efficiency of breast density assessment in mammography.
The approach's robustness is confirmed across different datasets.