CASIA OpenIR  > 中国科学院分子影像重点实验室
Classification of brain disease from magnetic resonance images based on multi-level brain partitions
Li T(李涛); Wensheng Zhang
2017
Conference NameIEEE Engineering in Medicine and Biology Society
Source PublicationBiomedical Imaging & Image Processing
Conference DateAugust 16-20, 2016
Conference PlaceOrlando, Florida, USA
Abstract
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.
 
KeywordBrain Disease Classification
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12807
Collection中国科学院分子影像重点实验室
Corresponding AuthorWensheng Zhang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Li T,Wensheng Zhang. Classification of brain disease from magnetic resonance images based on multi-level brain partitions[C],2017.
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