In general, the hidden Markov random field (HMRF) represents the class label distribution of an image in probabilistic model based segmentation. The class label distributions provided by existing HMRF models consider either the number of neighboring pixels with similar class labels or the spatial distance of neighboring pixels with dissimilar class labels. Also, this spatial information is only considered for estimation of class labels of the image pixels, while its contribution in parameter estimation is completely ignored. This, in turn, deteriorates the parameter estimation, resulting in sub-optimal segmentation performance. Moreover, the existing models assign equal weightage to the spatial information for class label estimation of all pixels throughout the image, which, create significant misclassification for the pixels in boundary region of image classes. In this regard, the paper develops a new clique potential function and a new class label distribution, incorporating the information of image class parameters. Unlike existing HMRF model based segmentation techniques, the proposed framework introduces a new scaling parameter that adaptively measures the contribution of spatial information for class label estimation of image pixels. The importance of the proposed framework is depicted by modifying the HMRF based segmentation methods. The advantage of proposed class label distribution is also demonstrated irrespective of the underlying intensity distributions. The comparative performance of the proposed and existing class label distributions in HMRF model is demonstrated both qualitatively and quantitatively for brain MR image segmentation, HEp-2 cell delineation, natural image and object segmentation.

中文翻译：

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鲁棒图像分割的空间受限概率模型

通常，隐藏的马尔可夫随机字段（HMRF）表示基于概率模型的分割中图像的类标签分布。现有HMRF模型提供的类别标签分布考虑具有相似类别标签的相邻像素的数量或具有不同类别标签的相邻像素的空间距离。而且，仅考虑该空间信息来估计图像像素的类别标签，而完全忽略其在参数估计中的贡献。反过来，这会使参数估计变差，从而导致次优的分割性能。此外，现有模型会为空间信息分配相等的权重，以估计整个图像中所有像素的类别标签，对图像类别边界区域中的像素造成严重的错误分类。在这方面，本文结合图像分类参数信息，开发了一种新的集团势函数和一种新的分类标签分布。与现有的基于HMRF模型的分割技术不同，该框架引入了新的缩放参数，该参数自适应地测量空间信息对图像像素的类别标签估计的贡献。通过修改基于HMRF的分割方法来描述所提出框架的重要性。不管基础强度分布如何，也证明了建议的类别标签分布的优势。