Interactive 3-dimensional segmentation of MRI data in personal computer environment
Identifieur interne : 007791 ( Main/Exploration ); précédent : 007790; suivant : 007792Interactive 3-dimensional segmentation of MRI data in personal computer environment
Auteurs : Seung-Schik Yoo [Corée du Sud] ; Chang-Uk Lee [Corée du Sud] ; Byung Gil Choi [Corée du Sud] ; Pairash Saiviroonporn [Thaïlande]Source :
- Journal of Neuroscience Methods [ 0165-0270 ] ; 2001.
English descriptors
Abstract
We describe a method of interactive three-dimensional segmentation and visualization for anatomical magnetic resonance imaging (MRI) data in a personal computer environment. The visual feedback necessary during 3-D segmentation was provided by a ray casting algorithm, which was designed to allow users to interactively decide the visualization quality depending on the task-requirement. Structures such as gray matter, white matter, and facial skin from T1-weighted high-resolution MRI data were segmented and later visualized with surface rendering. Personal computers with central processing unit (CPU) speeds of 266, 400, and 700 MHz, were used for the implementation. The 3-D visualization upon each execution of the segmentation operation was achieved in the order of 2 s with a 700 MHz CPU. Our results suggest that 3-D volume segmentation with semi real-time visual feedback could be effectively implemented in a PC environment without the need for dedicated graphics processing hardware.
Url:
DOI: 10.1016/S0165-0270(01)00470-8
Affiliations:
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The visual feedback necessary during 3-D segmentation was provided by a ray casting algorithm, which was designed to allow users to interactively decide the visualization quality depending on the task-requirement. Structures such as gray matter, white matter, and facial skin from T1-weighted high-resolution MRI data were segmented and later visualized with surface rendering. Personal computers with central processing unit (CPU) speeds of 266, 400, and 700 MHz, were used for the implementation. The 3-D visualization upon each execution of the segmentation operation was achieved in the order of 2 s with a 700 MHz CPU. 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