Matlab基于K均值聚类与区域合并的彩色图像分割算法-Fuzzy Clustering Technique with Spat....pdf-资料库

2010 Second International conference on Computing, Communication and Networking Technologies A ROBUST FUZZY CLUSTERING TECHNIQUE WITH SPATIAL NEIGHBORHOOD INFORMATION FOR EFFECTIVE MEDICAL IMAGE SEGMENTATION An Efficient Variants of Fuzzy Clustering Technique with Spatial Information for Effective Noisy Medical Image Segmentation S.Zulaikha Beevi 1, M.Mohammed Sathik 2, K.Senthamaraikannan 3 ,J.H.Jaseema Yasmin4 1 Assistant Professor, Department of IT, National College of Engineering, Tamilnadu, India. 2 Associate Professor, Department of Computer Science, Sathakathullah Appa College, Tamilnadu, India. 3 Professor & Head, Department of Statistics, Manonmaniam Sundaranar University, Tamilnadu, India. 4 Assistant Professor, Department of Computer Science, National College of Engineering, Tamilnadu, Inida. Abstract- Segmentation is an important step in many medical imaging applications and a variety of image segmentation techniques do exist. Of them, a group of segmentation algorithms is based on the clustering concepts. In our research, we have intended to devise efficient variants of Fuzzy C-Means (FCM) clustering towards effective segmentation of medical images. The enhanced variants of FCM clustering are to be devised in a way to effectively segment noisy medical images. The medical images generally are bound to contain noise while acquisition. So, the algorithms devised for medical image segmentation must be robust to noise for achieving desirable segmentation results. The existing variants of FCM-based algorithms, segment images without considering the spatial information, which makes it sensitive to noise. We proposed the algorithm, which incorporate spatial information into FCM, have shown considerable resilience to noise, yet with increased noise levels in images, these approaches have not performed exceptionally well. In the proposed research, the input noisy medical images are employed to a denoising algorithm with the help of effective denoising algorithm prior to segmentation. Moreover, the proposed approach will improve upon the existing variants of FCM-based segmentation algorithms by integrating the spatial neighborhood information present in the images for better segmentation. The spatial neighborhood images will be determined using a factor that represents the spatial influence of the neighboring pixels on the current pixel. The employed factor works on the assumption that the membership degree of a pixel to a cluster is greatly influenced by the membership of its neighborhood pixels. Subsequently, the denoised images will be segmented using the designed variants of FCM. The proposed segmentation approach will be robust to noisy images even at increased levels of noise, thereby enabling effective segmentation of noisy medical images. information of the 978-1-4244-6589-7/10/$26.00 ©2010 IEEE Index Terms - clustering, fuzzy C-means, image segmentation, membership function, variants. I.INTRODUCTION into different groups, or more precisely, Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classification of similar objects the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. Medical imaging techniques such as X - ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound (USG), etc. are indispensable for the precise analysis of various medical pathologies. Computer power and medical scanner data alone are not enough. We need the art to extract the necessary boundaries, surfaces, and segmented volumes these organs in the spatial and temporal domains. This art of organ extraction is segmentation. Image segmentation is essentially a process of pixel classification, wherein the image pixels are segmented into subsets by assigning the individual pixels to classes. These segmented organs and their boundaries are very critical in the quantification process for physicians and medical surgeons, in any branch of medicine, which deals with imaging [1]. Recently, fuzzy techniques are often applied as complementary to existing techniques and can contribute to the development of better and more robust methods, as it has been illustrated in numerous scientific branches. It seems to be proved that

applications of fuzzy techniques are very successful in the area of image processing [2]. Moreover, the field of medicine has become a very attractive domain for the application of fuzzy set theory. This is due to the large role imprecision and uncertainty plays in the field [3]. The main objective of medical image segmentation is to extract and characterize anatomical structures with respect to some input features or expert knowledge. Segmentation methods that includes the classification of tissues in medical imagery can be performed using a variety of techniques. Many clustering strategies have been used, such as the crisp clustering scheme and the fuzzy clustering scheme, each of which has its own special characteristics [3]. The conventional crisp clustering method restricts each point of the data set to exclusively just one cluster. However, in many real situations, for images, issues such as limited spatial resolution, poor contrast, overlapping intensities, noise and intensity in homogeneities variation make this hard (crisp) segmentation a difficult task. The fuzzy set theory [4], which involves the idea of partial membership described by a membership function, fuzzy clustering as a soft segmentation method has been widely studied and successfully applied to image segmentation [5–11]. Among the fuzzy clustering methods, fuzzy c-means (FCM) algorithm [1] is the most popular method used in image segmentation because it has robust characteristics for ambiguity and can retain much more than hard segmentation methods [5, 6].Although the conventional FCM algorithm works well on most noise-free images, it has a serious limitation: it does not incorporate any information about spatial context, which cause it to be sensitive to noise and imaging artifacts. In this paper, Improved Spatial FCM (ISFCM) clustering algorithm for image segmentation is proposed. The algorithm is developed by incorporating the spatial neighborhood information into the standard FCM clustering algorithm by a priori probability. The probability is given to indicate the spatial influence of the neighboring pixels on the centre pixel, which can be automatically decided in the implementation of the algorithm by the fuzzy membership. The new fuzzy membership of the current centre pixel is then recalculated with this probability obtained from above. The algorithm is initialized by a given histogram based FCM algorithm, which helps to speed up the convergence of the algorithm. Experimental results with medical images that the proposed method can achieve comparable results to those from many derivatives of FCM algorithm, that gives the method presented in this paper is effective. information II. RELATED WORKS There are huge amount of works related to enhancing the conventional FCM and other forms for image segmentation are found in the literature. Let us review some of them. Smaine Mazouzi and Mohamed Batouche [13] have presented an approach for improving range image segmentation, based on fuzzy regularization of the detected edges. Initially, a degraded version of the segmentation was produced by a new region growing- based algorithm. Next, the resulting segmentation was refined by a robust fuzzy classification of the pixels on the resulting edges which correspond to border of the extracted regions. Pixels on the boundary between two adjacent regions are labeled taking into account the two regions as fuzzy sets in the fuzzy classification stage, using an improved version of the Fuzzy C-Mean algorithm. The process was repeated for all region boundaries in the image. A two-dimensional fuzzy C- means (2DFCM) algorithm was proposed by Jinhua Yu and Yuanyuan Wang [14] for the molecular image segmentation. The 2DFCM algorithm was composed of three stages. The first stage was the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage was texture energy characterization using a Gabor wavelet method. The third stage was introducing spatial constraints provided by the denoising data and the textural information into the two- dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. Hadi Sadoghi Yazdi and Jalal A. Nasiri [15] have presented a fuzzy image segmentation algorithm. In their algorithm, human knowledge was used in clustering features for fuzzy image segmentation. In fuzzy clustering, the membership values of extracted features for each pixel at each cluster change proportional to zonal mean of membership values and gradient mean of adjacent pixels. The direction of membership variations are interaction. Their segmentation specified using human approach was applied for segmentation of texture and documentation images and the results have shown that the human interaction eventuates to clarification of texture and reduction of noise in segmented images.G.Sudhavani and Dr.K.Sathyaprasad [16] have described the application of a modified fuzzy C-means clustering algorithm to the lip segmentation problem. The modified fuzzy C-means algorithm was able to take the initial membership function from the spatially Successful segmentation of lip images was possible with their method. Comparative study of their modified fuzzy C-means was done with the traditional fuzzy C-means algorithm by using Pratt’s Figure of Merit. (2009) B.Sowmya and B.Sheelarani [9] have explained the task of segmenting any given color image using soft computing techniques. The most basic attribute for segmentation was for a luminance amplitude neighboring image the connected pixels.

the need monochrome image and color components for a color image. Since there are more than 16 million colors available in any given image and it was difficult to analyze the image on all of its colors, the likely colors are grouped together by image segmentation. For that purpose soft computing techniques have been used. The soft computing techniques used are Fuzzy C- Means algorithm (FCM), Possibilistic C - Means algorithm (PCM) and competitive neural network. A self estimation algorithm was developed for determining the number of clusters. Agus Zainal Arifin and Akira Asano [12] have proposed a method of image thresholding by using cluster organization from the histogram of an image. A similarity measure proposed by them was based on inter-class variance of the clusters to be merged and the intra-class variance of the new merged cluster. Experiments on practical images have illustrated the effectiveness of their method. (2006) An high speed parallel fuzzy C means algorithm for brain tumor image segmentation is presented by S. Murugavalli and V. Rajamani [17]. Their algorithm has the advantages of both the sequential FCM and parallel FCM for the clustering process in the segmentation techniques and the algorithm was very fast when the image size was large and also it requires less execution time. They have also achieved less processing speed and minimizing for accessing secondary storage compared to the previous results. The reduction in the computation time was primarily due to the selection of actual cluster centre and the accessing minimum secondary storage. (2006) T. Bala Ganesan and R. Sukanesh [18] have deals with Brain Magnetic Resonance Image Segmentation. Any medical image of human being consists of distinct regions and these regions could be represented by wavelet coefficients. Classification of these features was performed using Fuzzy Clustering method (FCM Fuzzy C-Means Algorithm). Edge detection technique was used to detect the edges of the given images. Silhouette method was used to find the strength of clusters. Finally, the different regions of the images are demarcated and color coded. (2008) H. C. Sateesh Kumar et al. [19] have proposed Automatic Image Segmentation using Wavelets (AISWT) to make segmentation fast and simpler. The approximation band of image Discrete Wavelet Transform was considered for segmentation which contains significant information of image. The Histogram based algorithm was used to obtain the number of regions and the initial parameters like mean, variance and mixing factor. The final parameters are obtained by using the Expectation and Maximization the approximation coefficients was determined by Maximum Likelihood function. Histogram specification was proposed by Gabriel Thomas [20] as a way to improve image segmentation. Specification of the final histogram was done relatively easy algorithm. The segmentation the input of and all it takes is the definition of a low pass filter and the amplification and attenuation of the peaks and valleys respectively or the standard deviation of the assumed Gaussian modes in the final specification. Examples showing better segmentation were presented. The attractive side of their approach was the easy implementation that was needed to obtain considerable better results during the segmentation process. III. DESCRIPTION OF EMPLOYED DENOISING ALGORITHM Usually, the medical images obtained from sensors are bound to contain noise and blurred edges. The process of segmentation is made more intricate, owing to the presence of these artifacts in medical images. Consequently, denoising images prior to segmentation perhaps produce better segmentation accuracy. Recently, Alessandro Foi et al [21] presented an efficient denoising algorithm, which is used in the proposed approach. Initially, the input noisy medical images are denoised using the above-mentioned denoising algorithm. A brief description of the denoising strategy employed in the proposed approach is provided in the subsequent subsection. A. Pointwise SA-DCT Denoising Since noise is an inevitable one in image acquisition, denoising plays an important role in increasing the quality of the image. Noise removal has been widely studied as a primary low-level image processing procedure and copious amount of denoising schemes have been proposed. In our approach, we employed an efficient Pointwise Shape Adaptive DCT denoising algorithm[21] . In order to preserve the image local structures in a better way, within the transform support. In this way, it ensures that data are represented sparsely in the transform domain, significantly improving the effectiveness of thresholding. Before we proceed is worth mentioning that the approach can be interpreted as a special kind of local model selection which is adaptive with respect both to the scale and to the order of the utilized model. Shape- adapted orthogonal polynomials are the most obvious choice for the local transform, as they are more consistent with the polynomial modeling used to determine its support. However, in practice, cosine bases are known to be more adequate for the modeling of natural images. In particular, when image processing applications are of concern, the use of computationally efficient transforms is paramount and, thus, the low-complexity SA-DCT and high robustness to noise. further, it IV. PROPOSED APPROACH FOR SPATIAL FUZZY C-MEANS CLUSTERING

A. The Conventional FCM Clustering is the process of finding groups in unlabeled dataset based on a similarity measure between the data patterns (elements) [12]. A cluster contains similar patterns placed together. The fuzzy clustering technique generates fuzzy partitions of the data instead of hard partitions. Therefore, data patterns may belong to several clusters, having different membership values with different clusters. The membership value of a data pattern to a cluster denotes similarity between the given data pattern to the cluster. Given a set of n data patterns, X = x1,…,xk,…,xn, the fuzzy clustering technique minimizes the objective function, O(U,C ): fcmO )C(U, = ( ) v n m )ic,k(x2d ∑ ∑ iku = = i k 1 1 (1) where xk is the k-th D-dimensional data vector, ci is the center of cluster i, uik is the degree of membership of xk in the i-th cluster, m is the weighting exponent, d (xk, ci) is the distance between data xk and cluster center ci, n is the number of data patterns, v is the number of clusters. The minimization of objective function J (U, C) can be brought by an iterative process in which updating of degree of membership uik and the cluster centers are done for each iteration. in information The theory of Markov random field says that pixels in the image mostly belong to the same cluster as their neighbors. The incorporation of spatial information in the clustering process makes the algorithm robust to noise and blurred edges. But when using spatial the clustering optimization function may converge in local minima, so to avoid this problem the fuzzy spatial c means algorithm is initialized with the Histogram based fuzzy c-means algorithm. The optimization function for histogram based fuzzy clustering is given in the equation 4. ( L v ∑ ∑ ilu = = i 1 1 ) )ic,(2d)H(m (4) hfcmO )C (U, l = l where H is the histogram of the image of L-gray levels. Gray level of all the pixels in the image lies in the new discrete set G= {0,1,…,L-1}. The computation of membership degrees of H(l) pixels is reduced to that of only one pixel with l as grey ilu and center for level value. The member ship function histogram based fuzzy c-means clustering can be calculated as. l = ilu = iku 1 ⎛V ⎜ ∑ ⎜ = J 1 ⎝ ikd jkd ⎞ ⎟ ⎟ ⎠ 1 − m 1 ic L ∑ == l 1 1 ⎛V ⎜ ∑ ⎜ = J 1 ⎝ lid ljd ( ) m ilu ( L ∑ ilu = l 1 (5) 1 − m 1 ⎞ ⎟ ⎟ ⎠ l ( ) lH ) m (2) ( n ∑ iku == k 1 ( n ∑ iku = k 1 ) m kx ) m ic (3) i∀ iku satisfies: ]1,0∈iku [ , v k∀ ∑ iku = i 1 = 1 and iku < n where < 0 n ∑ = k 1 Thus the conventional clustering technique clusters an image data only with the intensity values but it does not use the spatial information of the given image. B. Initialization (6) where level l lid is the distance between the center i and the gray C. Proposed Approach As histogram based FCM algorithm operates merely on the histogram of an image, it is faster than the conventional FCM that processes the entire data [22]. Despite the fact that, conventional FCM algorithm works well on the majority of noise-free images, it has a major drawback, (i.e.) it is highly sensitive to noise and many other imaging artifacts. The histogram-based FCM can be made more robust against noise and blurred edges by incorporating the spatial information into it. The objective the proposed segmentation approach is given by )CO(U, function of

fcmO )C(U, = ( v n s ∑ ∑ iku = = i k 1 1 ) m )ic,k(x2d (7) s iku = 1 − m 1 V ∑ = J 1 ⎛ ⎜ ⎜ ⎝ ikd jkd ⎞ ⎟ ⎟ ⎠ ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ ikP kN ∑ = z 1 kN ⎛ ⎜ ⎜ ⎜⎜ ⎝ ⎛ ⎞ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ 1 − m 1 V ∑ = J 1 ⎛ ⎜ ⎜ ⎝ izd jzd ⎞ ⎟ ⎟ ⎠ The spatial membership function is calculated using the equation (8). iku of the proposed ISFCM s ikP is the apriori probability that kth pixel belongs to ith where cluster and calculated as ikP = ( ) kiNN kN (9) where NNi(k) is the number of pixels in the neighborhood of kN is kth pixel that belongs to cluster i after defuzzification. izd is the the total number of pixels in the neighborhood. distance between ith cluster and zth neighborhood of ith Thus the center ic of each cluster is calculated as s s ic ( ) n ms ∑ iku kx == k 1 ( ) n ms ∑ iku = k 1 (10) Two kinds of spatial information are incorporated in the member ship function of conventional FCM. Apriori probability and Fuzzy spatial information Apriori probability: This parameter assigns a noise pixel to one of the clusters to which its neighborhood pixels belong. The noise pixel is included in the cluster whose members are majority in the pixels neighborhood. Fuzzy spatial information: In the equation (8) the second term in the denominator is the average of fuzzy membership of the neighborhood pixel to a cluster. Thus a pixel gets higher membership value when their neighborhood pixels have high membership value with the corresponding cluster. V. RESULTS AND DISCUSSION The proposed algorithm converges very quickly because it gets initial parameters form already converged histogram ⎞ ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟⎟ ⎟ ⎠ ⎠ (8) based FCM. The proposed approach is applied on three kinds of images real world images, synthetic brain MRI image, original brain MRI image. In all the images additive Gaussian white noise is added with noise percentage level 0%, 5%, 10%, and 15% and corresponding results are shown. The quality of segmentation of the proposed algorithm can be calculated by segmentation accuracy As given as. = sA 100× cN pT (11) Nc is the number of correctly classified pixels and Tp is the total image. Segmentation accuracy of FCM, proposed approach without denoising in segmenting synthetic brain MRI images with different noise level is shown in figure 1. total number pixels the given is the in a b c d e Fig.1. Segmentation results of 15% noise added synthetic brain MRI image. (a) 15% noise added synthetic image. (b) FCM segmentation (c) Proposed approach without denoising (d) Proposed approach with denoising (e) Base true.

) % n o i t a t n e m g e S ( y c a r u c c a 100 95 90 85 80 Segmentation accuracy vs Noise level 5% 10% 15% 20% Noise level FCM Proposed approach without denoising Proposed approach with denoising without denoising and using denoising in segmenting synthetic brain MRI images with different noise level. Fig.2. Segmentation accuracy of FCM, proposed approach a b c d Fig.3. Segmentation results of original noiseless brain MRI image. (a) Original brain MRI image with tumor. (b) FCM segmentation result (c) Proposed approach without denoising (d). Proposed approach with denoising a b c d e f Fig.4. Segmentation results of original brain MRI image. Without denoising algorithm (a) Original brain MRI image with tumor. (b) FCM segmentation with 0% noise (c) with 5% noise (d) with 10 % noise (e) with 15 % noise (f) with 20% noise

a b c d e d Fig.5. Proposed segmentation results of original brain MRI image with denoising (a) Original brain MRI image with tumor. (b) segmentation with 0% noise (c) with 5% noise (d) with 10 % noise (e) with 15 % noise (f) with 20% noise VI. CONCLUSION To overcome the noise sensitiveness of conventional FCM clustering algorithm, this paper presents an improved spatial fuzzy c mean clustering algorithm for image segmentation. The main fact of this algorithm is to incorporate the spatial neighborhood information into the standard FCM algorithm by a priori probability. The probability can be automatically decided in the algorithm based on the membership function of the centre pixel and its neighboring pixels. The major advantage of this algorithm are its simplicity, which allows it to run on large datasets. As we employ a fast FCM algorithm to initialize the ISFCM algorithm, the algorithm converges after several iterations. Experimental results show that the proposed method is effective and more robust to noise and other artifacts than the conventional FCM algorithm in image segmentation. REFERENCES [1] J.C.Bezdek , “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, New York 1981. [2] N.R.Pal and S.K.Pal , “A review on image segmentation technique”, Pattern Recognition 26(9), 1993, pp. 1277–1294. [3] Weina Wang, Yunjie Zhang, Yi li, Xiaona Zhang, “The global fuzzy c- means clustering algorithm”, [In] Proceedings of the World Congress on Intelligent Control and Automation, Vol. 1, 2006, pp. 3604–3607. [4] L.A.Zadeh, “Fuzzy sets”, Information and Control, Vol. 8, 1965, pp. 338– 353. [5] J.C.Bezdek, L.O.Hall,and L.P.Clarke, “Review of MR image segmentation techniques using pattern recognition”, Medical Physics 20(4), 1993, pp. 1033–1048. [6] N.Ferahta, A.Moussaoui,K., Benmahammed and V. Chen, “New fuzzy clustering algorithm appliedto RMN image segmentation”, International Journal of Soft Computing 1(2), 2006, pp. 137–142. [7] Y.A.Tolias and S.M.Panas, “On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system”, IEEE Signal Processing Letters 5(10), 1998, pp. 245–247. [8] Y.A.Tolias and S.M.Panas,”Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions”, IEEE Transactions on Systems, Man and Cybernetics, Part A 28(3), 1998, pp. 359–369. [9] B.Sowmya and B.Sheelarani, "Colour Image Segmentation Using Soft Computing Techniques", in proc. of Intl. Journal on Soft Computing Applications, no. 4, pp: 69- 80, 2009. [10] M.N.Ahmed, S.M.Yamany, N.Mohamed , A.A.Farag and T. Moriarty , “A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data”, IEEE Transactions on Medical Imaging 21(3), 2002, pp. 193–199. [11] D.Q.Zhang , S.C.Chen, Z.S.Pan and K.R. Tan,”Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation”, In Proc. of International Conference on Machine Learning and Cybernetics, Vol. 4, 2003, pp. 2189–2192. [12] Agus Zainal Arifin and Akira Asano, "Image segmentation by histogram thresholding using hierarchical cluster analysis", in proc. of Pattern Recognition Letters, vol. 27, no. 13. Oct. 2006, Doi: 10.1016/j.patrec.2006.02.022. [13] Smaine Mazouzi and Mohamed Batouche, "Range Image Segmentation Improvement by Fuzzy Edge Regularization", in proc. of Information Technology Journal, vol. 7, no. 1, pp: 84- 90, 2008. [14] Jinhua Yu and Yuanyuan Wang, "Molecular Image Segmentation Based on Improved Fuzzy Clustering", in proc. of International Journal on Biomedical Imaging, vol. 2007, no. 1, Jan. 2007. [15] Hadi Sadoghi Yazdi, A.Jalal and Nasiri, "Fuzzy Image Segmentation Using Human Interaction", in proc. of Journal on Applied Sciences Research, vol. 5, no. 7, pp: 722- 728, 2009. [16] G.Sudhavani and Dr.K.Sathyaprasad, "Segmentation of Lip Images by Modified Fuzzy C-means Clustering Algorithm", in proc. of IJCSNS International Journal on Computer Science and Network Security, vol.9, no.4, April 2009. [17] S. Murugavalli and V. Rajamani, "A High Speed Parallel Fuzzy C- Mean Algorithm for Brain Tumor Segmentation", in proc. of BIME

Journal, vol. 6, no. 1, December 2006. [18] T. Bala ganesan and R. Sukanesh, "Segmentation of Brain MR Images using Fuzzy Clustering Method with Sillhouette Method", in proc. of Journal on Engineering and Applied Sciences, vol. 3, no. 10, pp: 792- 795, 2008. [19] H. C. Sateesh Kumar, K. B. Raja, K.R.Venugopal and L. M. Patnaik, "Automatic Image Segmentation using Wavelets", in proc. of IJCSNS International Journal on Computer Science and Network Security, vol. 9, no. 2, Feb. 2009. [20] Gabriel Thomas, "Image Segmentation using Histogram Specification", in Proc.of . IEEE Int. Conf.on Image Processing, pp: 589- 592, 12- 15 Oct., San Diego, 2008. [21] Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian, “Pointwise Shape Adaptive DCT for High Quality Denoising and Deblocking of Grayscale and Color Images”, IEEE Transactions on Image Processing Vol. 16, No. 5, May 2007 [22] . Zhi-Kai Huang,Yun-Ming Xie,De-Hui Liu and Ling-Ying Hou," Using Fuzzy C-means Cluster for Histogram-Based Color Image segment -ation," In proceedings of the 2009 International Conference on Information Technology and Computer Science, Vol. 01, pp. 597-600 , 2009. [23] Kannan, Ramathilagam and Sathya, " Robust Fuzzy C-Means in Classifying Breast Tissue Regions", In proceedings of ARTCOM International Conference on Advances in Recent Technologies in Communication and Computing, pp.543-545,2009. [24] Zhi-bing Wang and Rui-hua Lu, " A New Algorithm for Image Segmentation Based on Fast Fuzzy C-Means Clustering," In proceedings of the 2008 International Conference on Computer Science and Software Engineering, Vol. 06,pp. 14-17, 2008. of Civil department Zulaikha Beevi S. received the B.E., the and Transportation Engineering, Institute of Road and Transport Technology, TamilNadu, India and M.Tech from the Department of Computer Science and IT , Manonmaniam Sundaranar University, TamilNadu, India in 1992 and 2005, respectively. She is currently pursuing the Ph.D. degree, working with Prof.M..Mohamed and Prof.K.Senthamarai Kannan. She is working as Assistant Professor in National College of Engineering , Tirunelveli, TamilNadu, India. Sathik of for Sathik from Department completed M.Mohamed from B.Sc.,and M.Sc.,degrees Mathematics, Department M.Phill., of Computer Science, M.Tech from Computer Science and IT ,M.S., from Department of Counseling and Psycho Therapy, and M.B.A degree and Psycho Therapy, and M.B.A degree from reputed institutions. He has two years working experience as a Coordinator M.Phill Computer Science Program, Directorate of Distance and Continuing Education, M.S.University. He served as Additional Coordinator in Indra Gandhi National Open University for four years. He headed the University Study Center for MCA Week End Courese, Manonmaniam Sundaranar University for 9 years. He has been with the department of Computer Science, Sadakathullah Appa College for 23 years.Now he is working as a Reader for the same department. He works in the field of Image Processing, specializing particularly . Dr.Mohamed Sathik M. guided 30 M.Phil Computer Science Scholors and guiding 14 Ph.D Computer Science Scholor from M.S.University, Tirunelveli, Barathiyar University, Coimbatore and Periyar Maniammai University, Tanjavur. He presented 12 papers in international conferences in image processing and presented 10 papers in national conferences. He published 5 papers in International Journals and 5 papers in proceedings with ISBN. in medical imaging

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Matlab基于K均值聚类与区域合并的彩色图像分割算法-Fuzzy Clustering Technique with Spat....pdf

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