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Open AccessArticle

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

1
School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China
2
Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(11), 578; https://doi.org/10.3390/e19110578
Received: 3 July 2017 / Revised: 17 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
MRI segmentation is critically important for clinical study and diagnosis. Existing methods based on soft clustering have several drawbacks, including low accuracy in the presence of image noise and artifacts, and high computational cost. In this paper, we introduce a new formulation of the MRI segmentation problem as a kernel-based intuitionistic fuzzy C-means (KIFCM) clustering problem and propose a new DNA-based genetic algorithm to obtain the optimal KIFCM clustering. While this algorithm searches the solution space for the optimal model parameters, it also obtains the optimal clustering, therefore the optimal MRI segmentation. We perform empirical study by comparing our method with six state-of-the-art soft clustering methods using a set of UCI (University of California, Irvine) datasets and a set of synthetic and clinic MRI datasets. The preliminary results show that our method outperforms other methods in both the clustering metrics and the computational efficiency. View Full-Text
Keywords: fuzzy C-means; intuitionistic fuzzy entropy; DNA genetic algorithm; images segmentation; MRI fuzzy C-means; intuitionistic fuzzy entropy; DNA genetic algorithm; images segmentation; MRI
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Zang, W.; Zhang, W.; Zhang, W.; Liu, X. A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation. Entropy 2017, 19, 578.

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