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An image processing framework for white matter segmentation in suspected Alzheimer's disease

Gnana Jebadas D, Albert Raj A

White Matter (WM) atrophy is a good marker of cognitive decline and progression of Alzheimer’s disease (AD). Precise segmentation of WM from structural Magnetic Resonance (MR) images is pivotal in the accurate quantification of WM atrophy. An image processing framework for the accurate segmentation of WM is proposed in this article. The proposed framework comprises background removal, restoration of the image with Non-Local Means (NLM) Filter, enhancement with Contrast Limited Adaptive Histogram Equalization (CLAHE), skull stripping and k-Means segmentation with histogram guided initialization. The framework exhibited a mean Dice Similarity Index (DSI) of 87.27% with a standard deviation of ± 5.74, on axial plane MR images of T1 series, from 30 subjects, against manual segmentation as ground truth.