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Disease classification: A probabilistic approach

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8 Author(s)
Rathi, Y. ; Med. Sch., Harvard Univ., Boston, MA, USA ; Malcolm, J. ; Bouix, S. ; McCarley, R.
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We describe a probabilistic technique for separating two populations whereby analysis is performed on affine-invariant representations of each patient. The method begins by converting each voxel from a high-dimensional diffusion weighted signal to a low-dimensional diffusion tensor representation. Three orthogonal measures that capture different aspects of the local tissue are derived from the tensor representation to form a feature vector. From these feature vectors, we form a probabilistic representation of each patient. This representation is affine invariant, thus obviating the need for registration of the images. We then use a Parzen window classifier to estimate the likelihood of a new patient belonging to either population. To demonstrate the technique, we apply it to the analysis of 22 first-episode schizophrenic patients and 20 normal control subjects. With leave-many-out cross validation, we find a detection rate of 90.91% (10% false positives).

Published in:

Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on

Date of Conference:

14-17 April 2010

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