Coarse Classification to Region-Scalable Refining for White Matter Lesions Segmentation in Multi-Channel MRI

Brain lesions, especially White Matter Lesions (WMLs) that mostly found on magnetic resonance images of elderly people, are not only associated with normal aging, but also with various geriatric disorders including cardiovascular diseases, vascular disease, psychiatric disorders and dementia. Quantitative analysis of WMLs in large clinical trials is crucial in scientific investigations of such neurological diseases as well as in studying aging processes. Exploiting the different appearances of WMLs in multiple modalities, we propose a novel coarse classification to region-scalable refining method to segment WMLs in Magnetic Resonance Imaging (MRI) sequences without user intervention. Specifically, a nonlinear voxel-wise classifier is trained based on intensity features extracted from multimodality MRI sequences, and tissues' probabilistic prior provided by partial volume estimate images in native space. By considering the prior that the WMLs almost exist in white matter, a rejection algorithm is then used to eliminate the false-positive labels from the initial coarse classification. To further segment precise lesions boundary and detect missing lesions, a region-scalable refining is finally employed to effectively segment the WMLs based on the previous initial contour. Compared with the manual segmentation results from an experienced neuroradiologist, the segmentations for real images of our proposal show desirable performances and high accuracy and provide competitive solution with stateof- the-art methods.