Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.
OBJECTIVE
Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important.


METHODS
In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans.


RESULTS
Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set.


CONCLUSIONS
Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.
Lao
Zhiqiang
Z
Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104, USA. Zhiqiang.Lao@uphs.upenn.edu
Shen
Dinggang
D
Liu
Dengfeng
D
Jawad
Abbas F
AF
Melhem
Elias R
ER
Launer
Lenore J
LJ
Bryan
R Nick
RN
Davatzikos
Christos
C
D000368:Aged; D000465:Algorithms; D001927:Brain Diseases / Q000175:diagnosis*; D005103:Expert Systems; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051188:Knowledge Bases; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve