首页> 外文会议>International conference on image processing and pattern recognition in industrial engineering;IPPRIE 2010 >Study on the application of MRF and Fuzzy Clustering as well as the D-S Theory to image segmentation of the human brain
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Study on the application of MRF and Fuzzy Clustering as well as the D-S Theory to image segmentation of the human brain

机译:MRF和模糊聚类以及D-S理论在人脑图像分割中的应用研究

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A new image segmentation method based on Markov Random Field (MRF) and Two-Dimensional Histogram Method of Fuzzy Clustering as well as Dempster-Shafer (D-S) evidence theory is presented in this paper.The application of Markov Random Field to image restoration and segmentation can effectively remove noise and get more accurate segmentation results; And the application of Fuzzy Clustering Theory together with Two-Dimensional Histogram image segmentation methods can get more satisfactory segmentation results; However, these two ways leads to different classification results while classifying the controversial pixels in images, so we can use the Dempster-Shafer evidence theory to assign the controversial points to the plausibility interval, and then divide them. This paper will adopt the above three theories to propose a human brain image segmentation research method. Experimental result shows that the method solves the problem of the class attribution of the controversial points, and the segmentation result is more in line with human vision.
机译:本文提出了一种基于马尔可夫随机场(MRF)和二维直方图模糊聚类以及Dempster-Shafer(DS)证据理论的图像分割新方法。马尔可夫随机场在图像复原和分割中的应用可以有效去除噪声,获得更准确的分割结果;结合模糊聚类理论和二维直方图图像分割方法,可以得到较满意的分割结果。但是,这两种方法在对图像中有争议的像素进行分类时会导致不同的分类结果,因此我们可以使用Dempster-Shafer证据理论将有争议的点分配给合理性区间,然后进行划分。本文将采用以上三种理论提出一种人脑图像分割研究方法。实验结果表明,该方法解决了有争议点的类别归因问题,并且分割结果更符合人的视觉。

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