Image segmentation by multi-level thresholding based on fuzzy entropy and genetic algorithm in cloud

In this paper, we describe a new soft computing method for segmentation of both gray level and color images by using a fuzzy entropy based criteria (cost function), the genetic algorithm, and the evolutionary computation techniques. The presented method allow us to find optimized set of parameters for a predefined cost function. Particularly, we found the optimum set of membership functions by maximizing the fuzzy entropy and based on the membership functions. Experimental results show that the offered method can reliably segment and give better threshold then Otsu Multi-Level thresholding.

[1]  Mita Nasipuri,et al.  A spatial fuzzy C-means algorithm with application to MRI image segmentation , 2015, 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR).

[2]  Sos S. Agaian,et al.  Thermal conditions of breast cancer progress-treatment , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[3]  Sos S. Agaian,et al.  Cross-entropy Histogram Equalization , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[4]  S. Agaian,et al.  Progressive Edge Detection on multi-bit images using polynomial-based binarization , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[5]  Ahmed S. Ghiduk,et al.  A novel approach based on genetic algorithms and region growing for magnetic resonance image (MRI) segmentation , 2013, Comput. Sci. Inf. Syst..

[6]  Soumajit Pramanik,et al.  Dynamic Image Segmentation using Fuzzy C-Means based Genetic Algorithm , 2011 .

[7]  Zhimin Wang,et al.  An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation , 2013, Comput. Vis. Image Underst..

[8]  P. D. Thouin,et al.  Survey and comparative analysis of entropy and relative entropy thresholding techniques , 2006 .

[9]  Curt H. Davis,et al.  A genetic image segmentation algorithm with a fuzzy-based evaluation function , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[10]  Mehdi Shadaram,et al.  Bright and dark distance-based image decomposition and enhancement , 2014, 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings.

[11]  Atilla Baskurt,et al.  Multi-component cross entropy segmentation for color image retrieval , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[12]  Patrick Benavidez,et al.  Improving visual SLAM algorithms for use in realtime robotic applications , 2014, 2014 World Automation Congress (WAC).

[13]  Hong Yan,et al.  A technique of three-level thresholding based on probability partition and fuzzy 3-partition , 2001, IEEE Trans. Fuzzy Syst..

[14]  Wenbing Tao,et al.  Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm , 2003, Pattern Recognit. Lett..

[15]  Scott T. Acton,et al.  Biomedical Image Analysis: Segmentation , 2007, Biomedical Image Analysis: Segmentation.

[16]  Bing-Fei Wu,et al.  Recursive Algorithms for Image Segmentation Based on a Discriminant Criterion , 2007 .

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

[18]  N. Senthilkumaran,et al.  Image Segmentation - A Survey of Soft Computing Approaches , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

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

[20]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[21]  Sos S. Agaian,et al.  Logarithmic Edge Detection with Applications , 2008, J. Comput..

[22]  Mihir Narayan Mohanty,et al.  Optimized Clustering Method for CT Brain Image Segmentation , 2014, FICTA.

[23]  Sos S. Agaian,et al.  Boolean Derivatives With Application to Edge Detection for Imaging Systems , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).