The nature of statistical learning theory
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Average brain models: a convergence study
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Statistical Shape Analysis Using Fixed Topology Skeletons: Corpus Callosum Study
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
The Journal of Machine Learning Research
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IEEE Transactions on Information Technology in Biomedicine
Brain morphometry by probabilistic latent semantic analysis
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
DTI based diagnostic prediction of a disease via pattern classification
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Biomarkers for identifying first-episode schizophrenia patients using diffusion weighted imaging
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Semisupervised probabilistic clustering of brain MR images including prior clinical information
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
The relevance voxel machine (RVoxM): A Bayesian method for image-based prediction
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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In this paper, we employ an anatomical parameterization of spatial warps to reveal structural differences between medical images of healthy control subjects and disease patients. The warps are represented as structure-specific 9-parameter affine transformations, which constitute a global, non-rigid mapping between the atlas and image coordinates. Our method estimates the structure-specific transformation parameters directly from medical scans by minimizing a Kullback-Leibler divergence measure. The resulting parameters are then input to a linear Support Vector Machine classifier, which assigns individual scans to a specific clinical group. The classifier also enables us to interpret the anatomical differences between groups, as we can visualize the discriminative warp that best differentiates the two groups. We test the accuracy of our approach on a data set consisting of Magnetic Resonance scans from 16 first episode schizophrenics and 17 age-matched healthy control subjects. The data set also contains manual labels for four regions of interest in both hemispheres: superior temporal gyrus, amygdala, hippocampus, and para-hippocampal gyrus. On this small size data set, our approach, which performs classification based on the MR images directly, yields a leave-one-out cross-validation accuracy of up to 90%. This compares favorably with the accuracy achieved by state-of-the-art techniques in schizophrenia MRI research.