1. Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
- Author
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Ong, Kokhaur, Young, David M, Sulaiman, Sarina, Shamsuddin, Siti Mariyam, Mohd Zain, Norzaini Rose, Hashim, Hilwati, Yuen, Kahhay, Sanders, Stephan J, Yu, Weimiao, and Hang, Seepheng
- Subjects
Data Management and Data Science ,Information and Computing Sciences ,Clinical Research ,Algorithms ,Brain ,Cluster Analysis ,Humans ,Magnetic Resonance Imaging ,White Matter - Abstract
White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value
- Published
- 2022