DocumentCode :
1864222
Title :
Brain MR image tumor segmentation with ventricular deformation
Author :
Xiao, Kai ; Hassanien, Aboul Ella ; Sun, Yan ; Ng, Edwin Kit Keong
Author_Institution :
Sch. of Software, Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2011
fDate :
25-27 Aug. 2011
Firstpage :
213
Lastpage :
217
Abstract :
This paper addresses the issue of the weak association between brain MRI intensity value and anatomical meaning of MR image pixels. By investigating the deformation on brain lateral ventricles and compression from tumor, the correlation between them is quantified and utilized. With the proposed feature extraction component, lateral ventricular deformation is transformed into an additional feature for brain tumor segmentation. Some comparative experiments using both supervised and unsupervised pattern recognition segmentation methods show the improved tumor segmentation accuracy in some image cases.
Keywords :
biomedical MRI; brain; correlation methods; feature extraction; image recognition; image segmentation; medical image processing; tumours; MR image pixel; brain MR image tumor segmentation; brain MRI intensity value; brain lateral ventricular deformation; correlation; feature extraction component; supervised pattern recognition segmentation method; tumor compression; unsupervised pattern recognition segmentation method; Biomedical imaging; Feature extraction; Image segmentation; Magnetic resonance imaging; Sensitivity; Shape; Tumors; MR image; MRI; brain tumor; deformation; feature; lateral ventricles; medical image analysis; segmentation; shape analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computer Communication and Processing (ICCP), 2011 IEEE International Conference on
Conference_Location :
Cluj-Napoca
Print_ISBN :
978-1-4577-1479-5
Electronic_ISBN :
978-1-4577-1481-8
Type :
conf
DOI :
10.1109/ICCP.2011.6047871
Filename :
6047871
Link To Document :
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