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Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model
M. Masroor Ahmed, Dzulkifli Bin Mohammad
Pages - 27 - 34     |    Revised - 15-02-2008     |    Published - 30-02-2008
Volume - 2   Issue - 1    |    Publication Date - February 2008  Table of Contents
White Matter (WM), Gray Matter (GM), Cerebrospinal Fluid (CSF)
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
CITED BY (100)  
1 Sampurno, J., Faryuni, I. D., & Ivansyah, O. (2016, March). Automated analysis of image mammogram for breast cancer diagnosis. In THE 4TH INTERNATIONAL CONFERENCE ON THEORETICAL AND APPLIED PHYSICS (ICTAP) 2014 (Vol. 1719, p. 030036). AIP Publishing.
2 Raj, E. M. S., & Kumaresan, M. (2016). Boundary Detection Algorithm For Brain Tumor Position And Area Detection Using OPENCV. International Journal of Applied Engineering Research, 11(7), 5326-5331.
3 Javed, A., Chai, W. Y., Alenezi, A. R., & Kulathuramaiyer, N. (2015). Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators. Appl. Math, 9(1), 213-222.
4 Naveen, A., & Velmurugan, T. A Survey on Medical Images Extraction using Parallel Algorithm in Data Mining.
5 Balakumar, B., & Raviraj, P. Automatic brain tumour MR image segmentation performance analysis using Threshold and Genetic algorithm.
6 Tembhekar, P. A., Thakare, M. N., & Dhande, S. A. Spatial Fuzzy Clustering With Level Set Method for MRI Image.
7 Bangare, S. L., Patil, M., Bangare, P. S., & Patil, S. T. Implementing Tumor Detection and Area Calculation in Mri Image of Human Brain Using Image Processing Techniques.
8 Karthick, C., Idrissyedismail, S., Arunkailasam, E., Dhanapal, S., & Devika, T. the international journal of science & technoledge.
9 Roy, S., Ganguly, D., Chatterjee, K., & Bandyopadhyay, S. K. (2015). Computerized White Matter and Gray Matter Extraction from MRI of Brain Image. Journal of Biomedical Science and Engineering, 8(09), 582.
10 Naik, P. P. S., & Gopal, T. V. (2015). Quantitative Analysis and Segmentation of Metastasis Brain Images using Hybrid Mean Shift Clustering. Indian Journal of Science and Technology, 8(35).
11 Rajesh, V., Venkat, B., Karan, V., & Poonkodi, M. (2015). Brain Tumor Segmentation and its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm. Fuzzy Systems, 7(4), 103-107.
12 Mane, V. M., Jadhav, D. V., & Kawadiwale, R. B. (2015). Preprocessing in Early Stage Detection of Diabetic Retinopathy Using Fundus Images. In Advancements of Medical Electronics (pp. 27-38). Springer India.
13 Maheshwari, D., Shah, A. A., Shaikh, M. Z., Chowdhry, B. S., & Memon, S. R. (2015). Extraction of Brain Tumour in MRI Images using Marker Controlled Watershed Transform Technique in MATLAB. Journal of Biomedical Engineering and Medical Imaging, 2(4), 9.
14 Yazdani, S., Yusof, R., Pashna, M., & Karimian, A. (2015, May). A hybrid method for brain MRI classification. In Control Conference (ASCC), 2015 10th Asian (pp. 1-5). IEEE.
15 Khanian, M., Feizi, A., & Davari, A. (2014). An optimal partial differential equations-based stopping criterion for medical image denoising. Journal of medical signals and sensors, 4(1), 72.
16 Zhang, H., Shen, Z., & Liang, X. (2014). The novel efficient catalyst for biodiesel synthesis from rapeseed oil. Kinetics and Catalysis, 55(3), 293-298.
17 Kumar, V. S. A., & Rao, T. C. S. Brain Tumor Extraction by K-Means Clustering Based On Morphological Image Processing.
18 Sheikh, A., Krishna, R. K., & Dutt, S. Energy Efficient Approach for Segmentation of Brain tumor Using Ant Colony Optimization. International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume, 1.
19 Nandhagopal, N. Probabilistic Neural Network Based Brain Tumor Detection and Classification System.
20 Ragul 2nd, K. An Efficient Multi-Kernel Fuzzy C-Means Algorithm to Analyze and Segment the fastidious Tumor region in Medical Images. methods, 1, 2.
21 Taneja, N., & Sahu, O. P. MRI Brain Tumor Segmentation Using Improved ACO.
22 Oo, S. Z., & Khaing, A. S. brain tumor detection and segmentation using watershed segmentation and morphological operation.
23 Halder, A., & Giri, C. (2014, January). Brain tumor detection using segmentation based Object labeling algorithm. In Electronics, Communication and Instrumentation (ICECI), 2014 International Conference on (pp. 1-4). IEEE.
24 Patel, J., & Doshi, K. (2014). A study of segmentation methods for detection of tumor in brain MRI. Advance in Electronic and Electric Engineering, 4(3), 279-284.
25 Yasmin, M., Sharif, M., Mohsin, S., & Azam, F. (2014). Pathological Brain Image Segmentation and Classification: A Survey. Current Medical Imaging Reviews, 10(3), 163-177.
26 Khanian, M., Feizi, A., & Davari, A. (2014). An Optimal PDEs-based denoising in medical image processing. Journal of Medical Signals and Sensors, 4(1), 72-83.
27 Ramesh, b. v., rajesh, v., abazeed, m., faisal, n., adel, a., zubair, s., ... & irwansyah, e. (2014). an effective technique for brain tumour segmentation and detection using cuckoo-based neuro-fuzzy classifier. journal of theoretical and applied information technology, 70(2).
28 Deepika, G. L. (2014). Brain Tumor Segmentation of Noisy MRI Images using Anisotropic Diffusion Filter.
29 Anami, B. S., & Unki, P. H. A Fuzzy-C-Means Approach for Tissue Volume Estimation in Brain MRI Images.
30 Shrivastava, K., Gupta, N., & Sharma, N. (2014). Medical Image Segmentation using Modified K Means Clustering. International Journal of Computer Applications, 103(16).
31 Tandel, R. B., Shah, D. U., & Shah, V. (2014). Detection of Breast Tumor from Mammographic Image Using K-Means Clustering Algorithm. Data Mining and Knowledge Engineering, 6(2).
32 Huang, M., Yang, W., Wu, Y., Jiang, J., Chen, W., & Feng, Q. (2014). Brain tumor segmentation based on local independent projection-based classification. Biomedical Engineering, IEEE Transactions on, 61(10), 2633-2645.
33 Ain, Q., Jaffar, M. A., & Choi, T. S. (2014). Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor. Applied Soft Computing, 21, 330-340.
34 Joseph, R. P., Singh, C. S., & Manikandan, M. (2014). brain tumor MRI image segmentation and detection in image processing. Int. J. Res. Eng. Technol, 3(1), 1-5.
35 Kawadiwale, R. B., & Rane, M. E. (2014). Clustering Techniques for Brain Tumor Detection.
36 Sivaramakrishnan, A., & Karnan, M. (2013). A novel based approach for extraction of brain tumor in MRI images using soft computing techniques. Journal of Advanced Research in Computer and Communication Engineering, 2(4), 1845-1848.
37 Krishna, A. N., & Prasadb, B. G. (2002). Region and Location Based Indexing and Retrieval of MR-T2 Brain Tumor Images. Pattern Recognition, 1, 528-531.
38 Christ, M. J., Subramanian, R., Thirumalvalavan, R., & Vignesh, A. Automatic Brain Tumor Segmentation by Variational Minimax Optimization Technique.
39 Nimeesha, K. M., & Gowda, R. M. Brain Tumour Segmentation Using K-Means And Fuzzy C-Means Clustering Algorithm.
40 Cecil, M. M., & Bandyopadhyay, S. K. gray matter and white matter relates to human emotions.
41 Neelum, N., Khizar, H., Sajjad, M., Hussain, M., Ahmad, I., & Alelaiwi, A. (2013). Curvelet based Brain Tumor Detection from Magnetic Resonance Images.
42 Soleimani, V., & Vincheh, F. H. (2013, March). Improving ant colony optimization for brain MRI image segmentation and brain tumor diagnosis. In Pattern Recognition and Image Analysis (PRIA), 2013 First Iranian Conference on (pp. 1-6). IEEE.
43 Vijay, J., & Subhashini, J. (2013, April). An efficient brain tumor detection methodology using K-means clustering algoriftnn. In Communications and Signal Processing (ICCSP), 2013 International Conference on (pp. 653-657). IEEE.
44 Gilanie, G., Attique, M., Naweed, S., Ahmed, E., & Ikram, M. (2013). Object extraction from T2 weighted brain MR image using histogram based gradient calculation. Pattern Recognition Letters, 34(12), 1356-1363.
45 Thomas, T., & Thomas, B. (2013). A Novel Automatic Method for Extraction of Glioma Tumor, White matter and Grey matter from Brain Magnetic Resonant Images. Biomedical Imaging and Intervention Journal, 9(2).
46 Cecil, M. M., & Bandyopadhyay, S. K. (2013). Utilizing WM and GM for Finding Intelligence of Human Behavior.
47 Hadadnia, J., & Rezaee, K. (2013). Extraction and 3D Segmentation of Tumors-Based Unsupervised Clustering Techniques in Medical Images. Iranian Journal of Medical Physics, 10(2), 95-108.
48 Javed, A., Alenezi, A. R., Wang, Y., & Kulathuramaiyer, N. (2013). Diagnosis System for the Detection of Abnormal Tissues from Brain MRI. Life Science Journal, 10(2).
49 Gupta, P., Gupta, J., Shringirishi, M., & Bhattnagar, D. (2013). Implementation of K-Means Clustering and Fuzzy C-Means Algorithm for Brain Tumor Segmentation. International Journal of Advanced Research in Computer Science, 4(8).
50 Patel, P. M., Shah, B. N., & Shah, V. (2013). Image segmentation using K-mean clustering for finding tumor in medical application. Int. J. Comput. Trends Technol, 4, 1239-1242.
51 Kamble, T., & Rane, P. (2013). Brain tumor segmentation using swarm intelligence approach. International Journal of Scientific&Engineering Research, 4(5).
52 Kyaw, M. M. (2013). Pre-segmentation for the computer aided diagnosis system. Int. J. Comput. Sci. Inf. Technol, 5(1).
53 Dorairangaswamy, M. A. (2013). An Extensive Review of Significant Researches on Medical Image Denoising Techniques. International Journal of Computer Applications, 64(14), 1-12.
54 Suji, G. E., Lakshmi, Y. V. S., & Jiji, G. W. (2013). Image Segmentation Algorithms on MR Brain Images. International Journal of Computer Applications, 67(16), 18-20.
55 Mohan, P., Al, V., Shyamala, B. R., & Kavitha, B. C. (2013). Intelligent based brain tumor detection using ACO. Int. J. Innov. Res. Comput. Commun. Eng, 1(9), 2143-2150.
56 Gupta, M. P., & Shringirishi, M. M. (2013). Implementation of brain tumor segmentation in brain mr images using k-means clustering and fuzzy c-means algorithm. International Journal of Computers & Technology, 5(1), 54-59.
57 Huo, J., Okada, K., van Rikxoort, E. M., Kim, H. J., Alger, J. R., Pope, W. B., ... & Brown, M. S. (2013). Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging. Medical physics, 40(9), 093502.
58 Selvaraj, D., & Dhanasekaran, R. (2013). Segmentation of cerebrospinal fluid and internal brain nuclei in brain magnetic resonance images. International Review on Computers and Software (IRECOS),8(5), 1063-1071.
59 Tirpude, N., & Welekar, R. R. (2013). A study of brain magnetic resonance image segmentation techniques. International Journal of Advanced Research In computer and communication engineering, 2(1).
60 Kabade, M. R. S., & Gaikwad, M. S. (2013). Segmentation of Brain Tumour and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm. International Journal of Computer Science & Engineering Technology (IJCSET), 4(05), 2229-334.
61 Roy, S., Nag, S., Maitra, I. K., & Bandyopadhyay, S. K. (2013). A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain. arXiv preprint arXiv:1312.6150.
62 Roy, S., Nag, S., Maitra, I. K., & Bandyopadhyay, S. K. (2013). International Journal of Advanced Research in Computer Science and Software Engineering. International Journal, 3(6).
63 Prasad, B. G. (2013). Region and Location Based Indexing and Retrieval of MR-T2 Brain Tumor Images. arXiv preprint arXiv:1312.2061.
64 Kshirsagar, V. A., & Panchal, J. R. Brain Tumour Segmentation Using Clustering Algorithms.
66 Smitha, J. C., & Babu, S. S. (2012). A Broad review of noteworthy researches on brain abnormality detection with the aid of medical images. Eur. J. Sci. Res, 85(2), 279-304.
67 Esfandian, N., Razzazi, F., & Behrad, A. (2012). A clustering based feature selection method in spectro-temporal domain for speech recognition. Engineering Applications of Artificial Intelligence, 25(6), 1194-1202.
68 Li, H., & Fan, Y. (2012, May). Label propagation with robust initialization for brain tumor segmentation. In Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on (pp. 1715-1718). IEEE.
69 Jaffar, M. A., Ain, Q., & Choi, T. S. (2012). Tumor detection from enhanced magnetic resonance imaging using fuzzy curvelet. Microscopy research and technique, 75(4), 499-504.
70 Beham, M. P., & Gurulakshmi, A. B. (2012, March). Morphological image processing approach on the detection of tumor and cancer cells. In Devices, Circuits and Systems (ICDCS), 2012 International Conference on (pp. 350-354). IEEE.
71 Attique, M., Gilanie, G., Hafeez-Ullah, M. S., Mehmood, M. S., Naweed, M. I., Kamran, J. A., ... & Zang, Y. F. (2012). Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues. PloS one, 7(3).
72 Resmi, S. A., & Thomas, T. (2012). A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI.
73 Selvakumar, J., Lakshmi, A., & Arivoli, T. (2012, March). Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm. In Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on (pp. 186-190). IEEE.
74 Dalmiya, S., Dasgupta, A., & Datta, S. K. (2012). Application of Wavelet based K-means Algorithm in Mammogram Segmentation. International Journal of Computer Applications, 52(15), 15-19.
75 Lehana, P., Devi, S., Singh, S., Abrol, P., Khan, S., & Arya, S. (2012). Investigations of the MRI images using aura transformation. Signal & Image Processing: An International Journal (SIPIJ), 3(1), 95-104.
76 Choubey, M., & Agrawal, S. (2012). An implementation of random walk algorithm to detect brain cancer in 2-d MRI. Magnetic resonance imaging, 2(1), 998-1001.
77 Ananda, R. S., & Thomas, T. (2012, October). Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques. In Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on (pp. 238-242). IEEE.
78 Yaping, L., Jinfang, Z., Fanjiang, X., & Xv, S. (2012, November). The recognition and enhancement of traffic sign for the computer-generated image. In Digital Home (ICDH), 2012 Fourth International Conference on (pp. 405-410). IEEE.
79 Javed, A., Chai, W. Y., & Kulathuramaiyer, N. contourlet transform based enhanced brain mr image segmentation.Javare, S., Patil, V., & Patil, N. Brain MR Image Segmentation Technique: A Review.
80 Salem, W. S., Seddik, A. F., & Ali, H. F. A Review on Brain MRI Image Segmentation.
81 Ibrahim, S., Khalid, N. E. A., & Manaf, M. Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia. Tel:+ 6019-2692717, Fax:+ 604-5941023,* E-mail: elaiza@ tmsk. uitm. edu. my.
82 Meenakshi, S. R., Mahajanakatti, A. B., & Bheemanaik, S. Morphological Image Processing Approach Using K-Means Clustering for Detection of Tumor in Brain.
83 Khalid, N. E. A., Ibrahim, S., & Haniff, P. N. M. M. (2011). MRI Brain Abnormalities Segmentation using K-Nearest Neighbors(k-NN). International Journal on Computer Science and Engineering, 3(2), 980-990.
84 Khalid, N. E. A., Ibrahim, S., & Manaf, M. (2011, July). Brain abnormalities segmentation performances contrasting: adaptive network-based fuzzy inference system (ANFIS) vs K-nearest neighbors (k-NN) vs fuzzy c-means (FCM). In 15th WSEAS International Conference on Computers (pp. 15-17).
85 Christ, M. J., & Parvathi, R. M. S. (2011, April). Fuzzy c-means algorithm for medical image segmentation. In Electronics Computer Technology (ICECT), 2011 3rd International Conference on (Vol. 4, pp. 33-36). IEEE.
86 Qureshi, A., & Kamdi, S. (2011). Segmentation of Medical Images Using Ant Colony Optimization.
87 Mirajkar, G., & Sonavane, Y. (2011). Optimal Feature Selection using Independent Component Analysis and Significant Feature Processing Applied to Human Brain MR Images. Advances in Computational Sciences & Technology, 4(4).
88 Sheikh, A., & Krishna, R. K. (2011). Segmentation of brain MRI for tumor detection using ant colony optimization. Proc. of Int. Colloquiums on Computer Electronics Electrical Mechanical and Civil.
89 Esteves, T., Valente, M., Nascimento, D. S., & Quelhas, P. (2011). Automatic and semi-automatic analysis of the extension of myocardial infarction in an experimental murine model. In Pattern Recognition and Image Analysis (pp. 151-158). Springer Berlin Heidelberg.
90 Li, H., Song, M., & Fan, Y. (2011). Segmentation of brain tumors in multi-parametric MR images via robust statistic information propagation. In Computer VisionACCV 2010 (pp. 606-617). Springer Berlin Heidelberg.
91 Noreen, N., Hayat, K., & Madani, S. A. (2011). MRI segmentation through wavelets and fuzzy C-means. World Applied Sciences Journal, 13, 34-39.
92 Ibrahim, S., Khalid, N. E. A., & Manaf, M. (2010). Seed-based region growing (SBRG) vs adaptive network-based inference system (ANFIS) vs fuzzy c-means (FCM): brain abnormalities segmentation. International Journal of Electrical and Computer Engineering, 5(2), 94-104.
93 Ibrahim, S., Khalid, N. E. A., & Manaf, M. (2010, March). Empirical study of brain segmentation using particle swarm optimization. In Information Retrieval & Knowledge Management,(CAMP), 2010 International Conference on (pp. 235-239). IEEE.
94 Khalid, N. E. A., Ibrahim, S., Manaf, M., & Ngah, U. K. (2010, June). Seed-based region growing study for brain abnormalities segmentation. In Information Technology (ITSim), 2010 International Symposium in (Vol. 2, pp. 856-860). IEEE.
95 Eqbal, S., & Balkrishnan, V. (2010). Detection of Brain Tumor Suspect Areas from CAT scan and MR Images Using CUDA.
96 Farmaki, C., Mavrigiannakis, K., Marias, K., Zervakis, M., & Sakkalis, V. (2010, November). Assessment of automated brain structures segmentation based on the mean-shift algorithm: Application in brain tumor. In Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on (pp. 1-5). IEEE.
97 Ain, Q. U., Mehmood, I., Naqi, S. M., & Jaffar, M. A. (2010). Bayesian classification using DCT features for brain tumor detection. In Knowledge-Based and Intelligent Information and Engineering Systems (pp. 340-349). Springer Berlin Heidelberg.
98 Jaffar, M. A., Masood, S., Iqbal, A., Javed, A., & Mirza, A. M. (2009, December). Fuzzy curvelet based fully automated segmentation of brain from MR images. In 2009 2nd International Conference on Computer Science and its Applications.
99 Mat Safri, N., Salleh, S. Z., & Ali, S. H. A. (2009). Moving one dimensional cursor using extracted parameter from brain signals. Signal Processing: An International Journal (SPIJ), 3(5), 110-119.
100 Salleh, S. Z., Safri, N. B. M., Ali, S. H. A., & Safri, N. M. (2009). Moving One Dimensional Cursor Using Extracted Parameter. Signal Processing: An International Journal (SPIJ), 3(5), 110-119.
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1 M. Mancas, B. Gosselin, B. Macq, 2005, "Segmentation Using a Region Growing Thresholding", Proc. of the Electronic Imaging Conference of the International Society for Optical Imaging (SPIE/EI 2005), San Jose (California, USA).
2 Dong-yong Dai; Condon, B.; Hadley, D.; Rampling, R.; Teasdale, G.; "Intracranial deformation caused by brain tumors: assessment of 3-D surface by magnetic resonance imaging"IEEE Transactions on Medical Imaging Volume 12, Issue 4, Dec. 1993 Page(s):693 702
3 http://noodle.med.yale.edu
4 Matthew C. Clark Segmenting MRI Volumes of the Brain With Knowledge- Based Clustering MS Thesis, Department of Computer Science and Engineering, University of South Florida, 1994
5 Dzung L. Pham, Chenyang Xu, Jerry L. Prince;"A Survey of Current Methods in Medical Medical Image Segmentation" Technical Report JHU / ECE 99-01, Department of Electrical and Computer Engineering. The Johns Hopkins University, Baltimore MD 21218, 1998.
6 http://documents.wolfram.com/
7 Chowdhury, M.H.; Little, W.D,;"Image thresholding techniques" IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing, 1995. Proceedings. 17-19 May 1995 Page(s):585 589
8 M. Sezgin, B. Sankur " Survey over image thresholding techniques and quantitative performance evaluation" J. Electron. Imaging 13 (1) (2004) 146-165.
9 Pan, Zhigeng; Lu, Jianfeng;;"A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation" Computing in Science & Engineering, Volume 9, Issue 4, July-Aug. 2007 Page(s):32 38
10 Zhou, J.; Chan, K.L.; Chong, V.F.H.; Krishnan, S.M Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005, Page(s):6411 6414
11 Velthuizen RP, Clarke LP, Phuphanich S, Hall LO, Bensaid AM, Arrington JA, Greenberg HM and Silbiger ML. "Unsupervised Tumor Volume Measurement Using Magnetic Resonance Brain Images," Journal of Magnetic Resonance Imaging , Vol. 5, No. 5, pp. 594-605, 1995.
12 J. C. Bezdek, L. O. Hall, L. P. Clarke "Review of MR image segmentation techniques using pattern recognition. " Medical Physics vol. 20, no. 4, pp. 1033 (1993).
13 Izquierdo, E.; Li-Qun Xu;Image segmentation using data-modulated nonlinear diffusion Electronics Letters Volume 36, Issue 21, 12 Oct. 2000 Page(s):1767 1769
14 Guillermo N. Abras and Virginia L. Ballarin,; "A Weighted K-means Algorithm applied to Brain Tissue Classification", JCS&T Vol. 5 No. 3, October 2005.
15 S. Wareld, J. Dengler, J. Zaers, C. Guttmann, W. Gil, J. Ettinger, J. Hiller, and R. Kikinis. Automatic identication of grey matter structures from mri to improve the segmentation of white matter lesions. J. of Image Guided Surgery, 1(6):326{338, 1995.
16 Perona, P.; Malik, J.; Scale-space and edge detection using anisotropic diffusion Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 12, Issue 7, July 1990 Page(s):629 639
17 Dmitriy Fradkin, Ilya Muchnik (2004)"A Study of K-Means Clustering for Improving Classification Accuracy of Multi-Class SVM". Technical Report. Rutgers University, New Brunswick, New Jersey 08854, April, 2004.
Mr. M. Masroor Ahmed
- Malaysia
Mr. Dzulkifli Bin Mohammad
- Malaysia