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Image Segmentation Using Variable Kernel Fuzzy C Means (VKFCM) Clustering On Modified Level Set Method

机译:基于改进水平集方法的可变核模糊C均值(VKFCM)聚类的图像分割

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In this paper, Variable Kernel Fuzzy C-Means (VKFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. Firstly, VKFCM algorithm computes the fuzzy membership values for each pixel. On the basis of VKFCM the edge indicator function was redefined. Using the edge indicator function the image segmentation of a medical image was performed to extract the regions of interest for further processing. The above process of segmentation showed a considerable improvement in the evolution of the level set function.
机译:在本文中,可变核模糊C均值(VKFCM)用于生成初始轮廓曲线,该曲线克服了曲线传播过程中边界处的泄漏。首先,VKFCM算法计算每个像素的模糊隶属度值。在VKFCM的基础上,重新定义了边缘指示器功能。使用边缘指示器功能对医学图像进行图像分割,以提取感兴趣区域以进行进一步处理。上面的分割过程显示出水平集功能的演变有很大的改进。

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