A novel fuzzy non-homogeneity measure based kernelized image segmentation for noisy images

The paper proposes a novel non-homogeneity measure based kernelized image segmentation algorithm for noisy images. Every 3×3 neighbourhood of every single pixel is considered for generating localized spatial domain non-homogeneity measures for every individual window. Then these spatial domain non-homogeneity measures are converted into fuzzy domain non-homogeneity coefficients by aggregating the localized measures into a single distribution and then deriving fuzzy domain values from a Gaussian membership function. Quantitative analyses have been rendered with respect to state-of-the-art noisy-image segmentation techniques and results show improved performance. Speckle-noise ridden SAR images and Rician noise ridden medical images are finally considered to show real-life applications of our algorithm.

[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]  Torbjørn Eltoft,et al.  Homomorphic wavelet-based statistical despeckling of SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Maoguo Gong,et al.  Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering , 2012, IEEE Transactions on Image Processing.

[4]  Daoqiang Zhang,et al.  Semi-supervised clustering with metric learning: An adaptive kernel method , 2010, Pattern Recognit..

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

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

[7]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

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

[9]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[10]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[11]  Fabrizio Argenti,et al.  Blind Speckle Decorrelation for SAR Image Despeckling , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[12]  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).

[13]  Jitendra Malik,et al.  Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Isabelle Bloch.  Fuzzy sets in image processing , 1994, SAC '94.

[15]  Volker Roth,et al.  Nonlinear Discriminant Analysis Using Kernel Functions , 1999, NIPS.

[16]  Hugo Guterman,et al.  An adaptive neuro-fuzzy system for automatic image segmentation and edge detection , 2002, IEEE Trans. Fuzzy Syst..

[17]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[18]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

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

[20]  Y. A. Tolias,et al.  On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system , 1998, IEEE Signal Processing Letters.

[21]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[22]  Korris Fu-Lai Chung,et al.  Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Hui Zhang,et al.  An entropy-based objective evaluation method for image segmentation , 2003, IS&T/SPIE Electronic Imaging.

[24]  Xiaowei Yang,et al.  A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises , 2011, IEEE Transactions on Fuzzy Systems.

[25]  Nor Ashidi Mat Isa,et al.  Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach , 2011, Pattern Recognit..

[26]  James C. Bezdek,et al.  A geometric approach to edge detection , 1998, IEEE Trans. Fuzzy Syst..