[An automatic 3D brain segmentation based on improved level-set method in micro-CT rat/mouse images].
OBJECTIVE
In vivo Micro-PETICT imaging of mouse/rat brain has been widely used to non-invasively monitor brain and provides researchers a better understanding of therapeutic effects in models of human neurological disease. For the need of further processing, extraction of brain tissue from head is required and vital.


METHODS
An automatic multistep combination methods was proposed based on an improved level set framework, which includes (1) Use Fuzzy-C-Means method together with threshold and morphology methods to get the initial level-set surface automatically. (2) Combine gradient vector flow to enhance the gradient contrast and enforce the surface move toward to the object's surface much faster, especially obtain a significantly improvement in the regions of forehead and the joint between brain and neck. (3) introduce an automatic stop condition based on average bandwidth energy maximization to overcome the leakage problem.


RESULTS
3 Micro-CT images of rat and 3 of mouse have been tested using the proposed methods and the average accuracy has increased by 33% for rat and 6.7% for mouse. The average processing duration for rat and mouse are about 8 minutes and 4 minutes, respectively.


CONCLUSIONS
The proposed methods were proved that it can be effectively used for Micro-PET/CT imaging of mouse/rat brain segmentation and have a great improvement on accuracy and convenience.
Department of Biomedical Engineering, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025.
Shiye
S
Chen
Cheng
C
Wang
Xiujuan
X
Zheng
D000818:Animals; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051379:Mice; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D055114:X-Ray Microtomography / Q000379:methods*