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
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; feature extraction; image resolution; image segmentation; medical image processing; tumours; MR image pixels; anatomical meaning; brain MR image tumor segmentation; brain MRI intensity value; brain lateral ventricles deformation; brain tumor segmentation; feature extraction component; lateral ventricular deformation; supervised pattern recognition segmentation method; unsupervised pattern recognition segmentation method; Biomedical imaging; Deformable models; Feature extraction; Image segmentation; Magnetic resonance imaging; Shape; Tumors; MR image; MRI; brain tumor; deformation; feature; lateral ventricles; medical image analysis; segmentation;
Conference_Titel :
Image and Graphics (ICIG), 2011 Sixth International Conference on
Conference_Location :
Hefei, Anhui
Print_ISBN :
978-1-4577-1560-0
Electronic_ISBN :
978-0-7695-4541-7
DOI :
10.1109/ICIG.2011.141