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

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Computerized segmentation method for individual calcifications within clustered microcalcifications while maintaining

Akiyoshi Hizukuri¹, Ryohei Nakayama, Nobuo Nakako

¹Graduate School of Engineering, Mie University, 1577 Kurimamachiya-cho, Tsu, Japan. hidukuri@ip.elec.mie-u.ac.jp

Journal of Digital Imaging

|October 13, 2011

Summary

A new computer-aided diagnosis (CADx) method accurately segments individual microcalcifications on mammograms, preserving their shapes. This technique enhances early detection of potential malignancy in clustered microcalcifications.

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Digital Mammography

Background:

Accurate segmentation of individual microcalcifications is crucial for computer-aided diagnosis (CADx) schemes evaluating mammograms.
Existing methods may struggle with segmenting microcalcifications of various sizes while preserving their distinct shapes.

Purpose of the Study:

To develop a computerized segmentation method for individual microcalcifications.
The method aims to maintain calcification shapes for improved accuracy in CADx schemes.
To enhance the detection of clustered microcalcifications on magnification mammograms.

Main Methods:

Mammogram images were decomposed into subimages using a filter bank.
Hessian matrix analysis was used to enhance nodular and linear components.
An artificial neural network utilized eight objective features for calcification enhancement.
Final segmentation was achieved using gray-level thresholding on the enhanced image.

Main Results:

The proposed method achieved a sensitivity of 96.5% for detecting calcifications within clusters.
The number of false positives per image was 1.69.
Average shape accuracy for segmented calcifications was 91.4%.

Conclusions:

The developed segmentation method effectively identifies individual microcalcifications with high sensitivity.
Preservation of calcification shapes contributes to the method's utility in CADx schemes.
This approach offers a valuable tool for improving mammogram analysis and malignancy assessment.