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Published on: September 25, 2019
Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection
1School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Insights
This study introduces a novel algorithm for detecting ischemic stroke lesions in CT scans. The method achieves high accuracy in classifying normal and abnormal brain regions, aiding in diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Ischemic stroke is a leading cause of death and disability.
- Computed tomography (CT) is crucial for rapid diagnosis and treatment planning.
- Accurate segmentation and classification of ischemic lesions are vital.
Purpose of the Study:
- To develop a novel histogram bin-based algorithm for segmenting ischemic stroke lesions in CT images.
- To identify optimal feature groups for classifying normal and abnormal brain regions.
- To improve the accuracy of ischemic stroke detection using machine learning classifiers.
Main Methods:
- Image pre-processing and segmentation of ischemic lesions.
- Extraction of texture features including first-order, gray level run length matrix, gray level co-occurrence matrix, and Hu's moments.
- Feature ranking, grouping, and selection for optimal classification.
- Classification using logistic regression, support vector machine, random forest, and neural network classifiers.
Main Results:
- The proposed algorithm effectively segments and classifies ischemic stroke lesions.
- Optimal feature group FG12 achieved high classification accuracies: 88.77% (LR), 97.86% (SVMC), 99.79% (RFC), and 99.79% (NNC).
- Results were validated using fourfold cross-validation, demonstrating robustness.
Conclusions:
- The developed histogram bin-based algorithm with optimal feature group selection is effective for ischemic stroke detection.
- Machine learning classifiers, particularly Random Forest and Neural Network, show high performance in classifying stroke lesions.
- This approach offers a promising tool for enhancing the diagnostic accuracy of ischemic stroke from CT images.
Abstract:
Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.
