Rough Possibilistic Type-2 Fuzzy C-Means clustering for MR brain image segmentation

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• 作者：Jnanendra Prasad Sarkar^a ; ¹ ; Indrajit Saha^b ; ^c ; ¹ ; ^{indra@icm.edu.pl" class="auth_mail" title="E-mail the corresponding author} ; Ujjwal Maulik^d ; ^{umaulik@cse.jdvu.ac.in" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fuzzy clustering ; Rough set ; Possibilistic ; Type-2 fuzzy set ; Statistical test
• 刊名：Applied Soft Computing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：46
• 期：Complete
• 页码：527-536
• 全文大小：6107 K

文摘

Pixel clustering in spectral domain is an important approach for the soft-tissue categorization of magnetic resonance (MR) brain images. In this regard, clustering algorithms based on type-1 fuzzy set theory are suitable for the overlapping partitions while the rough set based clustering algorithms deal with uncertainty and vagueness. However, additional degree of fuzziness makes the clustering more challenging for various subtle uncertainties and noisy data in the overlapping areas. Hence, this fact motivates us to propose a hybrid technique, called Rough Possibilistic Type-2 Fuzzy C-Means clustering with the integration of Random Forest. In the proposed method, possibilistic approach handles the noisy data better, whereas the other various uncertainties and inherent vagueness are taken care by type-2 fuzzy set and rough set theories. After clustering, it produces rough and crisp points. Thereafter, such crisp points are used to train the Random Forest classifier in order to classify the rough points for yielding better clustering solution. The performance of the proposed method has been demonstrated in comparison with several other recently proposed methods for MR brain image segmentation. Finally, superiority of the results produced by the proposed hybrid method has also been validated through statistical significance test.