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ISBI
2006
IEEE

Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification

11 years 3 months ago
Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification
This paper presents a fully automatic white matter lesion (WML) segmentation method, based on local features determined by combining multiple MR acquisition protocols, including T1-weighted, T2-weighted, proton density (PD)-weighted and fluid attenuation inversion recovery (FLAIR) scans. Support vector machines (SVMs) are used to integrate features from these 4 acquisition types, thereby identifying nonlinear imaging profiles that distinguish and classify WMLs from normal brain tissue. Validation on a population of 45 diabetes patients with diverse spatial and size distribution of WMLs shows the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from two experienced neuroradiologists.
Abbas Jawad, Bilge Karaçali, Christos Davat
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2006
Where ISBI
Authors Abbas Jawad, Bilge Karaçali, Christos Davatzikos, Dengfeng Liu, Dinggang Shen, Elias R. Melhem, R. Nick Bryan, Zhiqiang Lao
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