|
|
|
| A Fast FCM Cluster MultiThreshold Image Segmentation AlgorithmBased on Entropy Constraint |
| TIAN JunWei1,2, HUANG YongXuan1, YU YaLin3 |
1.School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 7100492.
School of Mechanic and Electronic Engineering, Xi'an Technological University,Xi'an 7100323.
Shanxi Huajing MicroElectronics Co.Ltd., Xi'an 710062 |
|
|
|
|
Abstract Aiming at the time consuming problem of traditional fuzzy cmean (FCM) cluster segmentation, a fast entropy constraint based fast FCM segmentation algorithm is proposed. The minimum resample ratio is studied, in which the sampled image can keep the most information of the initial image, and the limitation function of resample ratio is deduced. During calculation of the resample ratio, the relative entropy loss constraint of histogram is introduced to keep the sampled image out of serious distortion. Experiments are performed with entropy loss , and the results show that the multithreshold segmentation results of the proposed algorithm are almost as good as that of traditional FCM cluster segmentation algorithm. Moreover, the average consuming time of the proposed method is reduced greatly compared with traditional FCM algorithm, the calculational efficiency is increased obviously. The experiment results accord with the theory and the validity of the proposed algorithm is verified.
|
|
Received: 31 August 2006
|
|
|
|
|
|
[1] Pal N R, Pal S K. A Review on Image Segmentation Techniques. Pattern Recognition, 1993, 26(9): 12771294
[2] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York, USA: Plenum Press, 1981
[3] Udupa J K, Samarasekera S. Fuzzy Connectedness and Object Definition: Theory, Algorithm and Applications in Image Segmentation. Graph Models and Image Processing, 1996, 58(3): 246261
[4] Yamany S M, Farag A A, Hsu S. A Fuzzy Hyperspectral Classifier for Automatic Target Recognition (ATR) Systems. Pattern Recognition Letters, 1999, 20(11/12/13): 14311438
[5] Szilágyi L, Benyó Z, Szilágy S M, et al. MR Brain Image Segmentation Using an Enhanced Fuzzy cMeans Algorithm // Proc of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Cancun, Mexico, 2003, Ⅰ: 724726
[6] Chen Songcan, Zhang Daoqiang. Robust Image Segmentation Using FCM with Spatial Constraints Based on New KernelInduced Distance Measure. IEEE Trans on Systems, Man and Cybernetics, 2004, 34(4): 19071916
[7] Hathaway R J, Bezdek J C, Hu Yingkang. Generalized Fuzzy cMeans Clustering Strategies Using L Norm Distance. IEEE Trans on Fuzzy Systems, 2000, 8(5): 576582
[8] Leski J. An Insensitive Approach to Fuzzy Clustering. International Journal of Applied Mathematics and Computer Science, 2001, 11(4): 9931007
[9] Pham D L, Prince J L. Adaptive Fuzzy cMeans Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities. Proc of SPIE, 1998, 3338: 553563
[10] Tolias Y A, Panas S M. On Applying Spatial Constraints in Fuzzy Image Clustering Using a Fuzzy RuleBased System. IEEE Signal Processing Letters, 1998, 5(10): 245247
[11] Liew A W C, Leung S H, Lau W H. Fuzzy Image Clustering Incorporating Spatial Continuity. IEE Proc: Vision Image Signal Processing, 2000, 147(2): 185192
[12] Pham D L. Fuzzy Clustering with Spatial Constraints // Proc of the International Conference on Image Processing. New York, USA, 2002: Ⅱ: 6568
[13] Liew A W C, Yan Hong. An Adaptive Spatial Fuzzy Clustering Algorithm for 3D MR Image Segmentation. IEEE Trans on Medical Imaging, 2003, 22(9): 10631075
[14] Pham D L, Prince J L. Adaptive Fuzzy Segmentation of Magnetic Resonance Images. IEEE Trans on Medical Imaging, 1999, 18(9): 737752
[15] Ahmed M N, Yamany S M, Mohamed N, et al. A Modified Fuzzy cMeans Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Trans on Medical Imaging, 2002, 21(3): 193199
[16] Li Xiang, Li Liang, Lu Hongbing, et al. Inhomogeneity Correction for Magnetic Resonance Images with Fuzzy cMeans Algorithm. Proc of SPIE, 2003, 5032: 9951005
[17] He R, Datta S, Sajia B R, et al. Adaptive FCM with Contextual Constrains for Segmentation of MultiSpectral MRI // Proc of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco, USA, 2004: Ⅲ: 16601663
[18] Yen J C, Chang F J, Chang S. A New Criterion for Automatic Multilevel Thresholding. IEEE Trans on Image Processing, 1995, 4(3): 370378
[19] Li E H, Lee C K. Minimum CrossEntropy Thresholding. Pattern Recognition, 1993, 26(3): 617625
[20] Brink A D, Pendcock N E. Minimum CrossEntropy Threshold Selection. Pattern Recognition, 1996, 29(1): 179188
[21] Albuquerque M P, Esquet I A, Gesualdi M A R. Image Thresholding Using Tsallis Entropy. Pattern Recognition Letters, 2004, 25(9): 10591065 |
|
|
|
2008, Vol. 21 
