Comparative Study of Image Thresholding Using Type-2 Fuzzy Sets and Cloud Model

Uncertainty is an inherent part of image segmentation in real world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional image segmentation cannot fully handle the uncertainties. Type-2 fuzzy sets and cloud model can handle such uncertainties in a better way because they provide us with more design degrees of freedom. The paper presents a comparison on the two approaches for image segmentation with uncertainty, that is, image thresholding based on type-2 fuzzy sets and cloud model. Firstly, the theoretical foundations of two methods are analyzed. Secondly, the processing of image segmentation with uncertainty is compared through two stages respectively, which is histogram analysis and optimum threshold selection. Finally, the experiments are divided in three groups, both synthetic and real images are used to investigate the performance of handling uncertainty in image segmentation, and some noisy images are also involved in to validate the performance of suppressing noise. The experimental results suggest that the conclusion of comparisons is effective.

[1]  Martial Hebert,et al.  A Comparison of Image Segmentation Algorithms , 2005 .

[2]  Hamid R. Tizhoosh,et al.  Image thresholding using type II fuzzy sets , 2005, Pattern Recognit..

[3]  Jerry M. Mendel,et al.  Type-2 fuzzy sets and systems: an overview , 2007, IEEE Computational Intelligence Magazine.

[4]  Qin Kun,et al.  IMAGE SEGMENTATION BASED ON CLOUD MODEL , 2006 .

[5]  Tao Wu,et al.  On the methods of image segmentation with uncertainty , 2006, Geoinformatics.

[6]  Humberto Bustince,et al.  A Survey of Applications of the Extensions of Fuzzy Sets to Image Processing , 2009, Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition.

[7]  Arnaud Martin,et al.  Fusion for Evaluation of Image Classication in Uncertain Environments , 2006, 2006 9th International Conference on Information Fusion.

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[10]  Deyi Li,et al.  Artificial Intelligence with Uncertainty , 2004, CIT.

[11]  Hicham Laanaya,et al.  Evaluation for uncertain image classification and segmentation , 2006, Pattern Recognit..

[12]  Lorenzo Bruzzone,et al.  Image thresholding based on the EM algorithm and the generalized Gaussian distribution , 2007, Pattern Recognit..

[13]  Humberto Bustince,et al.  Comment on: "Image thresholding using type II fuzzy sets". Importance of this method , 2010, Pattern Recognit..

[14]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[15]  Korris Fu-Lai Chung,et al.  A novel image thresholding method based on Parzen window estimate , 2008, Pattern Recognit..

[16]  Frank Y. Shih,et al.  Image Segmentation , 2007, Encyclopedia of Biometrics.

[17]  Ioannis K. Vlachos,et al.  Comment on: "Image thresholding using type II fuzzy sets" , 2008, Pattern Recognit..

[18]  Phillip A. Laplante,et al.  A rough set-based approach to handling spatial uncertainty in binary images , 2004, Eng. Appl. Artif. Intell..

[19]  Yujin Zhang.  Chapter I An Overview of Image and Video Segmentation in the Last 40 Years , 2006 .

[20]  Kai Xu,et al.  An image segmentation approach based on histogram analysis utilizing cloud model , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[21]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  LiDeyi,et al.  Study on the Universality of the Normal Cloud Model , 2005 .

[23]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[24]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.