An image segmentation algorithm based on improved multiscale random field model in wavelet domain

Image segmentation is the key step in image analysis and image manipulating. Image segmentation based on multiscale random field model in wavelet domain (WMSRF) is a useful implementation tool. It can capture image structure information in different resolution and reduce the reliance on initial segmentation. However, WMSRF has boundary block effect and its operating efficiency is low. In this paper we propose an improved segmentation algorithm based on WMSRF (improved WMSRF). The improved WMSRF algorithm consists of two fields: the image characteristic field and the labeling field. The former is built on a series of boundary that is extracted by wavelet transform, and modeled by Gauss-MRF. The latter is also built on the boundary in corresponding scale, and modeled by multiscale random field (MSRF). Both fields constrain each other at the joint probability. This integrates interactions in inter-scale and inner-scale, and helps to describe image’s non-stationary property. Then the parameters in the models are estimated by using expectation–maximization. Consequently the segmentation result of initial image is achieved by using Bayesian and sequential maximum a posteriori estimation. In this paper, the medical images are utilized as experiment images. The simulations are compared with the WMSRF algorithm and the results show the improved algorithm can not only distinguish different regions effectively, but also improve the efficiency.

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