Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering

Computer Methods and Programs in Biomedicine

Volumn 108, Issue 3, 2012, Pages 1261-1276

  Hassan, Mehdi ^a   Chaudhry, Asmatullah ^b,c   Khan, Asifullah ^a   Kim, Jin Young ^c  

^a PAKISTAN INSTITUTE OF ENGINEERING AND APPLIED SCIENCES   (Pakistan)

^b PAKISTAN INSTITUTE OF NUCLEAR SCIENCE AND TECHNOLOGY   (Pakistan)

^c Chonnam National University   (South Korea)

Author keywords

Carotid artery; Ensemble clustering; Fuzzy c means; Genetic search; Neural networks

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; CAROTID ARTERY; CLUSTERING ACCURACY; DEGREE OF MEMBERSHIP; DISEASE DIAGNOSIS; ENSEMBLE CLUSTERING; FEATURE SETS; FUZZY C MEAN; GENETIC SEARCH; GRAY LEVEL CO-OCCURRENCE MATRIX; HIGHLY-CORRELATED; INTIMA-MEDIA THICKNESS; LEARNING CAPABILITIES; LOW QUALITIES; SEGMENTATION PROCESS; SEGMENTATION TECHNIQUES; SPATIAL INFORMATIONS; ULTRASOUND IMAGES; ULTRASOUND IMAGING; ULTRASOUND IMAGING SYSTEMS;

DIAGNOSIS; FUZZY SYSTEMS; NEURAL NETWORKS; PIXELS; ULTRASONIC IMAGING; ULTRASONICS;

IMAGE SEGMENTATION;

ALGORITHM; ARTERIAL WALL THICKNESS; ARTERIOGRAPHY; ARTICLE; BACK PROPAGATION; CAROTID ARTERY; CAROTID ARTERY DISEASE; CLASSIFICATION; COST; DIAGNOSTIC ACCURACY; ENTROPY; FUZZY SYSTEM; IMAGE QUALITY; IMAGING SYSTEM; PARTITION COEFFICIENT; PATTERN RECOGNITION; PERFORMANCE; PHANTOM; RECOGNITION; ULTRASOUND;

CAROTID ARTERIES; CLUSTER ANALYSIS; FUZZY LOGIC; HUMANS;

EID: 84868589804     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2012.08.011     Document Type: Article

References (42)

1. Chuang K.S., Tzeng H.L., Chen S., Wu J., Chen T.J. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society 2006, 30:9-15.
2. Iscan Z., Yüksel A., Dokur Z., Korürek M., Ölmez T. Medical image segmentation with transform and moment based features and incremental supervised neural network. Digital Signal Processing 2009, 19:890-901.
3. Vasantha M., Sunniah Bharathi V., Dhamodharan T. Medical image features, extraction, selection and classification. International Journal of Engineering Sciences and Technology 2010, 2:2071-2076.
4. Li B.N., Chui C.K., Chang S., Ong S.H. Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine 2011, 41:1-10.
5. Yu J., Tan J. Object density-based image segmentation and its applications in biomedical image analysis. Computer Methods and Programs in Biomedicine 2009, 96:193-204.
6. Cárdenes R., de Luis-García R., Bach-Cuadra M. A multidimensional segmentation evaluation for medical image data. Computer Methods and Programs in Biomedicine 2009, 96:108-124.
7. Kamel M., Campilho A., Abdel-Dayem A., El-Sakka M. Fuzzy c-means clustering for segmenting carotid artery ultrasound images. Image Analysis and Recognition 2007, Springer, Berlin, Heidelberg, pp. 935-948.
8. Kamel M., Campilho A., Abdel-Dayem A.R., El-Sakka M.R. Carotid artery ultrasound image segmentation using fuzzy region growing. Image Analysis and Recognition 2005, Springer, Berlin, Heidelberg, pp. 869-878.
9. Mao F., Gill J., Downey D., Fenster A. Segmentation of carotid artery in ultrasound images. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2000, 1734-1737.
10. Abolmaesumi P., Sirouspour M.R., Salcudean S.E. Real-time extraction of carotid artery contours from ultrasound images. 13th IEEE Symposium on Computer-Based Medical Systems, CBMS 2000, 181-186.
11. Hovda S., Rue H., Olstad B. New Doppler-based imaging method in echocardiography with applications in blood/tissue segmentation. Computer Methods and Programs in Biomedicine 2009, 96:12-24.
12. Hamou A.K., El-Sakka M.R. A novel segmentation technique for carotid ultrasound images. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04) 2004, pp. iii-521-524, vol. 523.
13. Canny J. A computational approach to edge detection. IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-8 1986, 679-698.
14. Abdel-Dayem A.R., Ei-Sakka M.R. A novel morphological-based carotid artery contour extraction. Canadian Conference on Electrical and Computer Engineering 2004, 1873-1876.
15. Abdel-Dayem A.R., El-Sakka M.R., Fenster A. Watershed segmentation for carotid artery ultrasound images. The 3rd ACS/IEEE International Conference on Computer Systems and Applications 2005, 131.
16. Vincent L., Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991, 13:583-598.
17. Christodoulou C.I., Pattichis C.S., Pantziaris M., Nicolaides A. Texture-based classification of atherosclerotic carotid plaques. IEEE Transactions on Medical Imaging 2003, 22:902-912.
18. Rocha R., Campilho A.l., Silva J., Azevedo E., Santos R. Segmentation of ultrasound images of the carotid using RANSAC and cubic splines. Computer Methods and Programs in Biomedicine 2011, 101:94-106.
19. Golemati S., Stoitsis J., Sifakis E., Balkizas T., Nikita K.S. Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. Ultrasound in Medicine and Biology 2007, 33:1918-1932.
20. Loizou C.P., Pattichis C.S., Pantziaris M., Nicolaides A. An integrated system for the segmentation of atherosclerotic carotid plaque. IEEE Transactions on Information Technology in Biomedicine 2007, 11:661-667.
21. Mougiakakou S.G., Golemati S., Gousias I., Nicolaides A.N., Nikita K.S. Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, Laws' texture and neural networks. Ultrasound in Medicine and Biology 2007, 33:26-36.
22. Hassan M., Chaudhry A., Khan A., Riaz S. An optimized fuzzy c-means clustering with spatial information for carotid artery image segmentation. 8th IBCAST 2011.
23. Gonzalez G., Woods E. Digital Image Processing 2009, Prentice Hall, Upper Saddle River, New Jersey. third ed.
24. Sheshadri H.S., Kandaswamy A. Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Computerized Medical Imaging and Graphics 2007, 31:46-48.
25. Polikar R. Fundamental Concepts & An Overview of the Wavelet Theory: Tutorial 1996, http://users.rowan.edu/~polikar/WAVELETS/WTpart1.html.
26. Ma J., Wang Z., Vo M., Luu L. Parameter discretization in two-dimensional continuous wavelet transform for fast fringe pattern analysis. Optics Info Base 2011, 50:6399-6408.
27. Holmes G., Donkin A., Witten I.H. WEKA: a machine learning workbench. Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems 1994, 357-361.
28. Bezdek H.L., Clarke J.L. Review of MR image segmentation using pattern recognition. Medical Physics 1993, 20:1033-1048.
29. Iyer N.S., Kandel A., Schneider M. Feature-based fuzzy classification for interpretation of mammograms. Fuzzy Sets and Systems 2000, 114:271-280.
30. Shaprio L.G., Stockman G.C. Computer Vision 2001, Prentice Hall, Upper Saddle River, NJ.
31. Kohonen T. Self Organizing and Associative Memory 1989, Springer Verlag, New York, USA.
32. Polikar R. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 2006, 6:21-45.
33. Davies D.L., Bouldin D.W. A cluster separation measure. IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-1 1979, 224-227.
34. Bezdek J.C. Cluster validity with fuzzy sets. Journal of Cybernetics 1973, 3:58-73.
35. Bazdek J.C. Mathematical models for systematic and taxonomy. International Conference on Numerical Taxonomy 1975, 143-166.
36. El Sherif M.S., Abdel Samee M.S. Pattern recognition using neural networks that learn from fuzzy rules. Proceedings of the 37th Midwest Symposium on Circuits and Systems 1994, 599-602.
37. Rudasi L., Zahorian S.A. Pattern recognition using neural networks with a binary partitioning approach. IEEE Proceedings of Southeastcon'91 1991, 726-730.
38. Takahashi H., Pecharanin N., Akima Y., Sone M. The reliability of neural networks on pattern recognition. IEEE World Congress on Computational Intelligence 1994, 3067-3070.
39. Greenhil D., Dvvies E.R. Relative effectiveness of neural networks for image noise suppression. Proceedings of the Pattern Recognition in Practice 1994, 367-378.
40. Jochem T.M., Pomerleau D.A., Thorpe C.E. Vision-based neural network road and intersection detection and traversal. IEEE/RSJ International Conference on Intelligent Robots and Systems 95 Human Robot Interaction and Cooperative Robots 1995, 344-349.
41. http://www.fantom.dk/.
42. Santhiyakumari N., Rajendran P., Madheswaran M. Medical decision-making system of ultrasound carotid artery intima-media thickness using neural networks. Journal of Digital Imaging 2011, 24:1112-1125.