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A Unified Framework for MR Based Disease Classification
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Institution: |
1Healthcare Informatics, IBM Almaden Research Center, San Jose, CA, USA. pohl@us.ibm.com 2Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. |
Publisher: |
Inf Process Med Imaging IPMI 2009 |
Publication Date: |
Jul-2009 |
Citation: |
Inf Process Med Imaging. 2009;21:300-13. |
PubMed ID: |
19694272 |
Appears in Collections: |
NAC, NA-MIC |
Sponsors: |
NIH NIBIB NAMIC U54 EB005149 NIH NCRR NAC P41 RR13218 NIH NINDS R01 NS051826 |
Generated Citation: |
Pohl K, Sabuncu M. A Unified Framework for MR Based Disease Classification. Inf Process Med Imaging. 2009;21:300-13. PMID: 19694272. |
| Downloaded: | 101 times. [view map] |
| Paper: | Download, View online |
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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.
Additional Material
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