Robust segmentation and intelligent decision system for cerebrovascular disease
Asmatullah Chaudhry1, Mehdi Hassan2,3, Asifullah Khan4
1DMIS, PAEC-HQ, Islamabad, Pakistan.
Medical & Biological Engineering & Computing
|April 9, 2016
Summary
This study introduces a robust method for segmenting and classifying carotid artery ultrasound images, improving cerebrovascular disease detection. The technique achieves high accuracy even with noisy images.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Ultrasound image analysis, particularly for carotid arteries, is crucial for diagnosing cerebrovascular diseases.
- Segmentation and classification of low-quality, noisy ultrasound images present significant challenges.
Purpose of the Study:
- To develop a robust technique for segmenting and classifying carotid artery ultrasound images.
- To accurately detect cerebrovascular disease by measuring intima-media thickness.
Main Methods:
- A two-phase approach: Expectation Maximization for label refinement, Genetic Algorithm for feature selection, and a Neuro-Fuzzy classifier for segmentation.
- A Support Vector Machine-based decision system for disease detection using intima-media thickness values.
Main Results:
- The proposed Robust Segmentation and Classification technique for Ultrasound images (RSC-US) achieved 98.84% accuracy, 0.988 F-measure, and 0.9767 MCC score.
- The method demonstrated robust performance across various noise levels.
Conclusions:
- The RSC-US technique offers a reliable solution for analyzing carotid artery ultrasound images, aiding in early cerebrovascular disease detection.
- The developed system is effective in segmenting and classifying noisy ultrasound data, with potential for clinical application.
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