Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Neural Tractography Using an Unscented Kalman Filter
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
A Unified Framework for MR Based Disease Classification
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Kernel-based manifold learning for statistical analysis of diffusion tensor images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
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Recent advances in diffusion weighted MR imaging (dMRI) has made it a tool of choice for investigating white matter abnormalities of the brain and central nervous system. In this work, we design a system that detects abnormal features (biomarkers) of first-episode schizophrenia patients and then classifies them using these features. We use two different models of the dMRI data, namely, spherical harmonics and the two-tensor model. The algorithm works by first computing several diffusion measures from each model. An affine-invariant representation of each subject is then computed, thus avoiding the need for registration. This representation is used within a kernel based feature selection algorithm to determine the biomarkers that are statistically different between the two populations. Confirmation of how well these biomarkers identify each population is obtained by using several classifiers such as, k-nearest neighbors, Parzen window classifier, and support vector machines to separate 21 first-episode patients from 20 age-matched normal controls. Classification results using leave-many-out cross-validation scheme are given for each representation. This algorithm is a first step towards early detection of schizophrenia.