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Updated: Mar 8, 2026

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

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A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological

Mohammad I Daoud¹, Tariq M Bdair¹, Mahasen Al-Najar²

¹Department of Computer Engineering, German Jordanian University, Amman, Jordan.

Computational and Mathematical Methods in Medicine

|January 28, 2017

Summary

This summary is machine-generated.

This study introduces a new computer-aided diagnosis (CAD) system for breast ultrasound (BUS) imaging. The system accurately differentiates benign and malignant tumors using advanced texture and morphological analysis, achieving high diagnostic performance.

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Breast ultrasound (BUS) imaging is crucial for cancer diagnosis but faces challenges in accuracy and operator dependency.
Computer-aided diagnosis (CAD) systems offer a potential solution to enhance diagnostic accuracy by providing radiologists with a second opinion.

Purpose of the Study:

To develop and evaluate a novel CAD system for accurate classification of breast ultrasound images.
To improve the differentiation between benign and malignant breast tumors using automated image analysis.

Main Methods:

Implementation of an improved texture analysis by dividing tumors into regions of interest (ROIs) for analysis using gray-level cooccurrence matrix features and a support vector machine classifier.
Integration of morphological analysis for tumor classification.
Fusion of classification results from multi-ROI texture analysis and morphological analysis using a probabilistic approach.

Main Results:

The proposed CAD system was applied to 110 BUS images (64 benign, 46 malignant).
Achieved high diagnostic performance with an overall accuracy of 98.2%, specificity of 98.4%, and sensitivity of 97.8%.

Conclusions:

The developed CAD system effectively differentiates benign and malignant breast tumors.
The proposed approach demonstrates significant potential for improving the accuracy and reliability of breast cancer diagnosis using ultrasound imaging.