Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.

An information theory perspective on computational vision

This paper introduces computer vision from an information theory perspective.We discuss how vision can be thought of as a decoding problem where the goal is to find the most efficient encoding of the visual scene.This requires probabilistic models which are capable of capturing the complexity and ambiguities of natural images.We start by describing classic Markov Random Field(MRF)models of images.We stress the importance of having efficient inference and learning algorithms for these models and emphasize those approaches which use concepts from information theory.Next we introduce more powerful image models that have recently been developed and which are better able to deal with the complexities of natural images.These models use stochastic grammars and hierarchical representations.They are trained using images from increasingly large databases.Finally,we described how techniques from information theory can be used to analyze vision models and measure the effectiveness of different visual cues.