Abstract:

BACKGROUND: The method based on Markov random field model is an important segmentation algorithm for medical images. The prior parameters of Gibbs field can severely affect the accuracy of image segmentation.
OBJECTIVE: Based on the properties of medical images, to explore the estimation method of Gibbs field prior parameter to improve the accuracy of segmentation.
METHODS: The relation between the variance of Gauss noises and the optimal Gibbs field prior parameters for brain MR images was obtained by a statistical method. In the image segmentation iterative steps, the Gibbs field prior parameters were estimated by means of interpolation using the estimation of image variance.
RESULTS AND CONCLUSION: Simulated brain MR images with different noise levels and real brain MR images are presented in the experiments. The results show that the proposed estimation method is easy in practical implementation, faster in computational speed, and is capable of achieving finer and adaptive segmentation compared with conventional methods.