An automated method for segmenting white matter lesions through multi-level morphometric feature classification with application to lupus

作者：Scully Mark^*; Anderson Blake; Lane Terran; Gasparovic Charles; Magnotta Vince; Sibbitt Wilmer; Roldan Carlos; Kikinis Ron; Bockholt Henry J

来源：Frontiers in Human Neuroscience, 2010, 4: 27.

DOI：10.3389/fnhum.2010.00027

• lupus
• lesion
• segmentation
• machine learning
• support vector machine
• classification
• method

We demonstrate an automated, multi-level method to segment white matter brain lesions and apply it to lupus. The method makes use of local morphometric features based on multiple MR sequences, including T1-weighted, T2-weighted, and fluid attenuated inversion recovery. After preprocessing, including co-registration, brain extraction, bias correction, and intensity standardization, 49 features are calculated for each brain voxel based on local morphometry. At each level of segmentation a supervised classifier takes advantage of a different subset of the features to conservatively segment lesion voxels, passing on more difficult voxels to the next classifier. This multi-level approach allows for a fast lesion classification method with tunable trade-offs between sensitivity and specificity producing accuracy comparable to a human rater.

• 出版日期2010-4