DocumentCode :
3129834
Title :
Graph Based Classification of MRI Data Based on the Ventricular System
Author :
Long, S. Seth ; Holder, Lawrence B.
Author_Institution :
Washington State Univ., Pullman, WA, USA
fYear :
2011
fDate :
11-11 Dec. 2011
Firstpage :
1034
Lastpage :
1041
Abstract :
The ventricular system inside the brain is known to enlarge and change shape given conditions such as Alzheimer´s disease. This change in shape may provide a way to assess the level of cognitive impairment of a patient, as well as other intellectual characteristics. This paper describes the use of trees to represent the 3D space containing the third and lateral ventricles, and classification of these trees using frequent sub graph mining and support vector machines. Level of cognitive impairment and years of education are shown to be predictable given a tree representation of the shape of the third and lateral ventricles, demonstrating that the shape of the ventricular system correlates with these attributes. These results were generated using a cross-sectional collection of 416 MR images of subjects ranging in age from 18 to 96 years, including 100 subjects diagnosed with Alzheimer´s disease.
Keywords :
biomedical MRI; brain; cognition; data mining; diseases; image classification; image representation; medical image processing; solid modelling; support vector machines; trees (mathematics); 3D space; Alzheimer disease; MR images; MRI data; brain; cognitive impairment; graph based classification; intellectual characteristics; lateral ventricles; subgraph mining; support vector machines; tree classification; tree representation; ventricular system; Accuracy; Alzheimer´s disease; Humans; Shape; Support vector machines; Three dimensional displays; Vectors; graph mining; mri; subgraph classification; support vector machine; ventricular system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4673-0005-6
Type :
conf
DOI :
10.1109/ICDMW.2011.90
Filename :
6137494
Link To Document :
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