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

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Computer Aided Detection of Clustered Microcalcification: A Survey.

M N Arun Kumar¹, M N Anil Kumar², H S Sheshadri³

¹Department of Computer Science and Engineering, Federal Institute of Science and Technology, Ernakulam, India.

Current Medical Imaging Reviews

|January 25, 2020

Summary

This review summarizes image processing and data mining techniques for pectoral muscle segmentation and microcalcification detection in mammograms. These methods aid radiologists in early breast cancer detection, improving diagnostic accuracy.

Keywords:

Computer aided detection classifier digital mammogram image processing microcalcification microcalcification cluster

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Biomedical Engineering

Background:

Mammography is crucial for early breast cancer detection in women over 40.
Microcalcifications (MCs) and their clusters (MCCs) are key indicators of malignant growths.
Radiologists face challenges in accurately assessing large volumes of mammograms.

Purpose of the Study:

To review techniques for pectoral muscle (PM) segmentation in digital mammograms.
To summarize methods for microcalcification (MC) detection and classification.
To highlight the role of computer-aided detection (CAD) in improving diagnostic accuracy.

Main Methods:

Image processing techniques for PM segmentation.
Data mining approaches for MC detection and classification.
Review of automated recognition methods for MCCs.

Main Results:

Identified various image processing and data mining techniques for PM segmentation.
Summarized methods for MC detection and classification in digital mammograms.
Highlighted the utility of automated MC detection for diagnostic purposes.

Conclusions:

The reviewed techniques are essential for automated analysis of mammograms.
Automated detection and classification of MCs can significantly assist radiologists.
Pectoral muscle segmentation is a key step in mammogram analysis.