模糊马尔可夫场模型与图像分割新算法

Fuzzy Markov random filed model and a new algorithm for image segmentation

本文建立模糊马尔可夫场模型,并提出基于模糊马尔可夫场的图像分割新算法。该算法同时处理模糊性和随机性,因此能有效获取图像的先验知识。在模糊马尔可夫场与待分割图像之间用经典的马尔可夫场关联。模糊马尔可夫场是经典马尔可夫场的推广,当模糊马尔可夫场失去模糊性时,它将退化为经典的马尔可夫场。给定图像,随即进行模糊化处理;以最大后验概率作为优化准则修正模糊马尔可夫场的隶属度;最后按照最大隶属度原则消除模糊性,从而得到图像的分割。该算法可以有效地虑除噪和消除部分容积效应,得到更为准确的分割结果。

A fuzzy Markov random field （FMRF） model is established and a new algorithm based on FMRF for image segmentation proposed in this paper. This algorithm simultaneously deals with the fuzziness and randomness for effective acquisition of the prior knowledge of the images. A conventional Markov random field （CMRF） serves as a bridge between the FMRF, obviously a generalization of the CMRF, and the original images. The FMRF degenerates into the CMRF when no fuzziness is considered. The segmentation results are obtained by fuzzifying the image, updating the membership of prior FMRF based on the maximum posteriori criteria, and defuzzifying the image according to the maximum membership principle. The proposed algorithm can effectively filter the noise and eliminate partial volume effect when processing the degraded image to ensure more accurate image segmentation.