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首页> 外文期刊>Computational and mathematical methods in medicine >A New Kernel-Based Fuzzy Level Set Method for Automated Segmentation of Medical Images in the Presence of Intensity Inhomogeneity
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A New Kernel-Based Fuzzy Level Set Method for Automated Segmentation of Medical Images in the Presence of Intensity Inhomogeneity

机译：基于新的基于内核的模糊水平集方法，用于在强度不均匀性存在下医学图像的自动分割

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Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based FuzzyC-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
机译：研究人员最近应用一条综合的方法来自动化医学图像细分，以便有利于可用的方法和消除其缺点。强度不均匀性是这一领域的一个具有挑战性和开放的问题，通过这种方法感到不那么关注。它对分割准确性有相当大的影响。本文提出了一种基于新的内核的模糊级别集算法来处理这个问题。它可以从基于Gaussian内核的Fuzzyc-mance（GKFCM）获得的初始级别集直接发展。还从GKFCM的结果估计了级别集进化的控制参数。此外，基于描述实际图像组成的图像模型，该算法通过局部正则化演化增强了局部正则化的演化，其中假设强度不均匀性作为图像的组件。这种改进使水平设置操作更容易，并导致强度不均匀的更强大的分割。该算法具有有价值的益处，包括自动化，强度不均匀性，高精度。所提出的算法的性能评估在不同方式的医学图像上进行。结果证实了其对医学图像分割的有效性。

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