A multiobjective approach to MR brain image segmentation
A multiobjective approach to MR brain image segmentation
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DOI：
10.1016/j.asoc.2010.01.007
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
A. Mukhopadhyay;U. Maulik
中科院分区：
文献类型：
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作者：
A. Mukhopadhyay;U. Maulik
本文提出了一种新颖的多目标实编码遗传模糊聚类方案，用于人脑多光谱磁共振图像（MRI）的分割。所提出的技术能够随着聚类结果自动演化聚类数量。多目标变串长度聚类技术将聚类中心编码在其染色体中，同时优化聚类之间的全局模糊紧致性和模糊分离度。在最后一代中，它产生一组非支配解决方案，从中选择最近提出的有效性指数 I 方面的最佳解决方案作为最终的聚类解决方案。相应的染色体长度提供了簇的数量。该方法应用于许多模拟的 T1 加权、T2 加权和质子密度加权正常和 MS 病变 MRI 脑图像。所提出的方法相对于 K 均值、模糊 C 均值、期望最大化、层次聚类、单目标遗传聚类和其他最新多目标聚类算法的优越性已得到定量证明。通过所提出的聚类技术获得的自动分割还将与可用的地面实况信息进行比较。
This article proposes a novel multiobjective real coded genetic fuzzy clustering scheme for segmentation of multispectral magnetic resonance image (MRI) of the human brain. The proposed technique is able to automatically evolve the number of clusters along with the clustering result. The multiobjective variable string length clustering technique encodes the cluster centers in its chromosomes and simultaneously optimizes the global fuzzy compactness and fuzzy separation among the clusters. In the final generation, it produces a set of non-dominated solutions, from which the best solution in terms of a recently proposed validity index I is chosen to be the final clustering solution. The corresponding chromosome length provides the number of clusters. The proposed method is applied on many simulated T1-weighted, T2-weighted and proton density-weighted normal and MS lesion MRI brain images. Superiority of the proposed method over K-means, Fuzzy C-means, Expectation Maximization, hierarchical clustering, Single Objective Genetic clustering and other recent multiobjective clustering algorithms has been demonstrated quantitatively. The automatic segmentation obtained by the proposed clustering technique is also compared with the available ground truth information.