Patch-based fuzzy clustering for image segmentation

Fuzzy C-means has been adopted for image segmentation, but it is sensitive to noise and other image artifacts due to not considering neighbor information. In order to solve this problem, many improved algorithms have been proposed, such as fuzzy local information C-means clustering algorithm (FLICM). However, the segmentation results of FLICM are unsatisfactory when performed on complex images. To overcome this, a novel fuzzy clustering algorithm is proposed in this paper, and more information is utilized to guide the procedure of image segmentation. In the proposed algorithm, pixel relevance based on patch similarity will be investigated firstly, by which all information over the whole image can be considered, not limited to local context. Compared with Zhang et al. (Multimed Tools Appl 76(6):7869–7895, 2017a. https://doi.org/10.1007/s11042-016-3399-x) pixel relevance is unnecessary to be normalized, and much more information can play positive role in image segmentation. Experimental results show that the proposed algorithm outperforms current fuzzy algorithms, especially in enhancing the robustness of corresponding fuzzy clustering algorithms.

[1]  S.M. Szilagyi,et al.  MR brain image segmentation using an enhanced fuzzy C-means algorithm , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[2]  Howard Besser,et al.  Visual Access to Visual Images: The UC Berkeley Image Database Project , 1990 .

[3]  Jin Li,et al.  Secure Deduplication with Efficient and Reliable Convergent Key Management , 2014, IEEE Transactions on Parallel and Distributed Systems.

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

[5]  Gang Wang,et al.  Improved fuzzy clustering algorithm with non-local information for image segmentation , 2017, Multimedia Tools and Applications.

[6]  Dzung L. Pham,et al.  Spatial Models for Fuzzy Clustering , 2001, Comput. Vis. Image Underst..

[7]  Fei Wu,et al.  Determining Image Base of Firmware Files for ARM Devices , 2016, IEICE Trans. Inf. Syst..

[8]  Yuanzhang Li,et al.  Determining image base of firmware for ARM devices by matching literal pools , 2016, Digit. Investig..

[9]  Licheng Jiao,et al.  Fuzzy c-means clustering with non local spatial information for noisy image segmentation , 2011, Frontiers of Computer Science in China.

[10]  Zexuan Ji,et al.  A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image , 2011, Comput. Medical Imaging Graph..

[11]  Feng Zhao,et al.  Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation , 2013, Neurocomputing.

[12]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[13]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Siu-Ming Yiu,et al.  Multi-key privacy-preserving deep learning in cloud computing , 2017, Future Gener. Comput. Syst..

[15]  Jin Li,et al.  Insight of the protection for data security under selective opening attacks , 2017, Inf. Sci..

[16]  Yuanzhang Li,et al.  DPPDL: A Dynamic Partial-Parallel Data Layout for Green Video Surveillance Storage , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[18]  Xinbo Gao,et al.  A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation , 2011, Signal Process..

[19]  Jean-Michel Morel,et al.  Nonlocal Image and Movie Denoising , 2008, International Journal of Computer Vision.

[20]  Gang Wang,et al.  An improved fuzzy algorithm for image segmentation using peak detection, spatial information and reallocation , 2017, Soft Comput..

[21]  Stelios Krinidis,et al.  A Robust Fuzzy Local Information C-Means Clustering Algorithm , 2010, IEEE Transactions on Image Processing.

[22]  Zexuan Ji,et al.  A framework with modified fast FCM for brain MR images segmentation , 2011, Pattern Recognit..

[23]  Jin Li,et al.  Location-Sharing Systems With Enhanced Privacy in Mobile Online Social Networks , 2017, IEEE Systems Journal.

[24]  Caiming Zhang,et al.  Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification , 2015, Comput. Math. Methods Medicine.

[25]  Caiming Zhang,et al.  Medical image segmentation using improved FCM , 2012, Science China Information Sciences.

[26]  Jin Li,et al.  Securely Outsourcing Attribute-Based Encryption with Checkability , 2014, IEEE Transactions on Parallel and Distributed Systems.

[27]  Maoguo Gong,et al.  Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation , 2013, IEEE Transactions on Image Processing.

[28]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[29]  Junyu Dong,et al.  Reconstruction of normal and albedo of convex Lambertian objects by solving ambiguity matrices using SVD and optimization method , 2016, Neurocomputing.

[30]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

[31]  Jin Li,et al.  Privacy-preserving outsourced classification in cloud computing , 2017, Cluster Computing.

[32]  J. Bezdek.  Cluster Validity with Fuzzy Sets , 1973 .