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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

Automatic detection of abnormalities in mammograms.

Zobia Suhail¹, Mansoor Sarwar², Kashif Murtaza³

¹Punjab University College of Information Technology (PUCIT), University of the Punjab, Lahore, Pakistan. zobia.suhail@pucit.edu.pk.

BMC Medical Imaging

|November 8, 2015

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A new low-cost Computer Aided Diagnostic (CAD) system effectively detects calcifications in mammograms. This method uses a novel "Ratio Energy" feature for efficient identification of microcalcification and macrocalcification, aiding early breast cancer diagnosis.

Area of Science:

Medical Image Processing
Computer Aided Diagnosis (CAD)

Background:

Rising interest in medical image processing and CAD systems for diagnostic assistance.
High cost of existing CAD systems, particularly in developing countries, necessitates low-cost solutions.
Mammogram classification often focuses on detecting calcification and abnormal masses as early breast cancer indicators.

Purpose of the Study:

To develop a low-cost Computer Aided Diagnostic (CAD) system for mammogram classification.
To introduce an efficient technique for detecting calcifications, a key early symptom of breast cancer.
To improve the accessibility of diagnostic tools in resource-limited settings.

Main Methods:

Proposed a scale-specific blob detection technique utilizing supervised learning for scale selection.

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Introduced a novel feature, "Ratio Energy," computed from pixel energy at two scales for efficient blob detection.

Achieved linear running time complexity with respect to image size due to feature simplicity and post-processing.

Main Results:

Successfully identified and highlighted two types of calcification: microcalcification and macrocalcification.
Results visualized using circular boundaries around detected calcification areas, ensuring clarity for radiologists.
Demonstrated visible and satisfactory detection outcomes.

Conclusions:

The developed CAD system aids radiologists in verifying diagnoses by identifying calcifications.
The new method leverages the clustered, small-size property of microcalcification for detection.
The system offers encouraging results for the early detection of breast cancer.