On Classifying Disease-Induced Patterns in the Brain Using Diffusion Tensor Images

Authors:
Peng Wang;Ragini Verma
Affiliations:
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, 19104 and Department of Integrated Data Systems, Siemens Corporate Research, , Princeton, 0 ...;Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, 19104
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

Citing 4
Cited 2

Bayes Error Estimation Using Parzen and k-NN Procedures

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support-Vector Networks

Machine Learning
An introduction to variable and feature selection

The Journal of Machine Learning Research
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

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
HARDI based pattern classifiers for the identification of white matter pathologies

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Diffusion tensor imaging (DTI) provides rich information about brain tissue structure especially in the white matter, which is known to be affected in several diseases like schizophrenia. Identifying patterns of brain changes induced by pathology is therefore crucial to clinical studies. However, the high dimensionality and complex structure of DTI make it difficult to apply conventional linear statistical and pattern classification methods to identify such patterns. In this paper, we present a novel framework that uses a combination of DTI-based anisotropy and geometry features to effectively identify brain regions with pathology-induced abnormality, and to classify brains into the diseased and healthy groups. Our method first directly estimates the underlying overlap between the patient and control groups, based on a semi-parametric Bayes error estimation method. By ranking voxels based on these estimation results, the method identifies abnormal brain regions from which features are extracted through Kernel Principal Component Analysis (KPCA) for subsequent classification. Application of the method to a dataset of controls and patients with schizophrenia, demonstrates promising accuracy of this framework in identifying brain patterns to separate two groups, and hence aiding in prognosis and treatment.