Abstract #5390

Automatic White Matter Hyperintensity extraction in ICEM with Orthogonal Subspace Projection-Based Go-Decomposition Approach

Yi-Ying Wu^1,2, Yen-Chieh Ouyang², Jyh-Wen Chai^1,3, Kuan- Jung Pan¹, Hsin-Che Wang⁴, Wen- Hsien Chen¹, Hsian-Min Chen⁴, Chi-Chang Clayton Chen^1,5, and Chein-I Chang⁶

¹Department of Medical Imaging, Taichung Veterans General Hospital, Taichung, Taiwan, ²Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan, ³Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan, ⁴Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan, ⁵Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan, ⁶Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, United States

Synopsis

Keywords:

Motivation: White matter hyperintensities (WMH) are associated with Alzheimer's disease and cognitive decline. Accurate and automated WMH detection is crucial for diagnosis and monitoring.

Goal(s): To automate WMH segmentation using the OSP-GoDec method and ICEM, replacing manual feature selection and comparing performance with SPM-LPA.

Approach: MRI data from Alzheimer's patients were preprocessed and augmented using the CBEP algorithm to generate hyperspectral images. OSP-GoDec identified high-signal WMH points, which were used to be training samples for ICEM-based automated segmentation.

Results: The proposed method achieved higher accuracy in detecting WMH, especially smaller lesions, compared to SPM-LPA, demonstrating its effectiveness for automated analysis.

Impact: Accurate automated detection of WMH can significantly improve diagnosis and monitoring of AD, enhancing clinical decision-making. This approach reduces reliance on manual segmentation, minimizes human error, and enables faster, more reliable analysis of brain MRI scans for better patient outcomes.

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