In this paper, we present a classification method based on the multi-level brain partitions. Bag-of-visual-words model is used. Firstly, the representative SIFT features are extracted from brain template as the basic visual words. Secondly, individual MR images are described using the basic visual words and support vector machine classifiers are trained for different brain partitions respectively. Thirdly, the final classification is derived from the combination of multiple classifiers. We apply this method to MR images of Alzheimer's disease and Parkinson's disease. The results demonstrate that the multi-level partitions favors the classification accuracy of brain disease from MR images.
['Tao Li', 'Wensheng Zhang']
Classification of brain disease from magnetic resonance images based on multi-level brain partitions