Abstract
The precise segmentation of cerebral vessels is essential for the detection of cerebrovascular diseases. The complex structures of cerebral vessels and the low contranst of thin vessels in medical images make precise segmentation difficult. In this study, we propose a new phase-field and statistical model for blood vessel segmentation. The proposed model is based on the Allen-Chan equation with double well potential and statistical distribution function. The brain tissues in the image are modeled by Gaussian distribution while cerebral vessels are modeled by uniform distribution respectively. The region distribution information combined with the phase-field model is used in curve evolution. And the level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Comparisons with the LBF model and LCV model show that our model can achieve better results with fewer iteration number and less time.
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