Title :
Anatomical-driven segmentation of the 3rd and 4th ventricles in MR data
Author :
Dong, Chun ; Newman, Timothy S.
Author_Institution :
Dept. of Comput. Sci., Alabama Univ., Huntsville, AL, USA
Abstract :
A method for automatic segmentation of the brain´s 3rd and 4th ventricles in MRI (magnetic resonance imaging) datasets is introduced. The method exploits anatomical knowledge about these structures and uses gradient-based edge detection and volume-growing to complete the segmentation. Nearby anatomic landmarks, including the longitudinal fissure, cerebellum and callosum are also automatically extracted in our approach. The method has been tested on a variety of T1- and T2- weighted MR images
Keywords :
biomedical MRI; brain; edge detection; image segmentation; medical image processing; MR data; MRI datasets; T1-weighted MR images; T2-weighted MR images; anatomic landmarks; anatomical knowledge; anatomical-driven segmentation; automatic segmentation; callosum; cerebellum; fourth ventricle; gradient-based edge detection; longitudinal fissure; magnetic resonance imaging; third ventricle; volume-growing; Computed tomography; Image recognition; Image segmentation; Surgery; Torso;
Conference_Titel :
[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-5674-8
DOI :
10.1109/IEMBS.1999.804334
