Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation

Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. However, the standard FCM algorithm takes a long time to partition a large dataset. In addition, in current fuzzy cluster algorithms it is difficult to determine the cluster centers. This paper proposes a modified FCM algorithm for MR (magnetic resonance) brain images segmentation. This method fetches in statistic histogram information for minimizing the iteration times, and in the iteration process, the optimal number of clusters is automatically determined. Using this method, an optimal classification rate is obtained in the test dataset, which includes large stochastic noises. The experiment results have shown that the segmentation method proposed in this paper is more accurate and faster than the standard FCM or the fast fuzzy c-means (FFCM) algorithm.

[1]  Yao-Hong Tsai.  A New Approach for Image Thresholding under Uneven Lighting Conditions , 2007, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007).

[2]  Nualsawat Hiransakolwong,et al.  Unsupervised Image Segmentation Using Automated Fuzzy c-Means , 2007, 7th IEEE International Conference on Computer and Information Technology (CIT 2007).

[3]  Ming Li,et al.  Fuzzy-C-Means Clustering Based On The Gray And Spatial Feature For Image Segmentation , 2006, 2006 International Conference on Computational Intelligence and Security.

[4]  Miin Shen Yang,et al.  Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms. , 2002, Magnetic resonance imaging.

[5]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[6]  Dao-Qiang Zhang,et al.  A novel kernelized fuzzy C-means algorithm with application in medical image segmentation , 2004, Artif. Intell. Medicine.

[7]  Lotfi A. Zadeh,et al.  Fuzzy Logic , 2009, Encyclopedia of Complexity and Systems Science.

[8]  Punya Thitimajshima,et al.  A new modified fuzzy c-means algorithm for multispectral satellite images segmentation , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[9]  Gu Jian-wen.  A New Algorithm for Image Segmentation Based on Modified Fuzzy C-Means Clustering , 2005 .

[10]  Yannis A. Tolias,et al.  Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[11]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

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

[13]  L. Vermeiren,et al.  Tissue classification for MRI of thigh using a modified FCM method , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.