Title :
Thalamus Segmentation from MRI Images by Lagrangian Surface Flow
Author :
Heckenburg, Greg ; Xi, Yongjian ; Duan, Ye ; Hua, Jing ; Muzik, Otto
Abstract :
In this paper, we present a new thalamus segmentation method for MRI images based on a new type of deformable model-Lagrangian surface flow. Given a MRI image, the user can interactively initialize a seed model within region of interest. The model will then start to grow according to both boundary and region information based on the principle of variational analysis. The deformation will stop when an equilibrium state is achieved. Our experiments demonstrate that the new method is robust to image noise and inhomogeneity and will not get stuck into local minima or leak from spurious edge gaps
Keywords :
biomedical MRI; brain; image segmentation; medical image processing; neurophysiology; variational techniques; Lagrangian surface flow; MRI images; image noise; spurious edge gaps; thalamus segmentation; variational analysis; Biomedical imaging; Cerebral cortex; Deformable models; Geometry; Image segmentation; Information analysis; Lagrangian functions; Magnetic resonance imaging; Noise robustness; Parkinson´s disease;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1617115
