Graph theory based algorithm for magnetic resonance brain images segmentation

8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008

Volumn , Issue , 2008, Pages

  Wang, Jianzhong ^a   Liu, Di ^a   Dou, Lili ^a   Zhang, Baoxue ^a   Kong, Jun ^a   Lu, Yinghua ^a  

^a NORTHEAST NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN IMAGES; CLINICAL ANALYSIS; COMPUTER AIDED; GRAPH REPRESENTATION; IMAGE REGIONS; MAGNETIC RESONANCE IMAGES; MEDICAL IMAGES; MR IMAGES; NORMALIZED CUTS; PROCESS USE; REGION SIMILARITY; SEGMENTATION METHODS; WATER-SHED ALGORITHM; WATERSHED TRANSFORM;

BIOINFORMATICS; COMPUTER AIDED ANALYSIS; GRAPH THEORY; IMAGE SEGMENTATION; LANDFORMS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; WATERSHEDS;

ALGORITHMS;

EID: 67549123696     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2008.4696839     Document Type: Conference Paper

References (26)

1. Chaozhe, Z. Jiang,T, "Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images", NeuroImage., 2003, 18 (3), 685-696.
2. Shan Shen, William Sandham, Malcolm Granat and Annette Sterr, "MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization", IEEE Transaction on Information Technology in Biomedicine, vol. 9, No. 3 (2005) 459-467.
3. Wells III WM, Grimson WEL, Kikinis R, Jolesz FA, "Adaptive segmentation of MRI data", IEEE Trans, Med. Imag, (1996) 15: 429-442.
4. Pham DL, Xu CY, Prince JL, "A survey of current methods in medical image segmentation", Ann. Rev. Biomed, Eng. 2 (2000) 315-37 [Technical report version, JHU/ECE 99-01, Johns Hopkins University].
5. Alan Wee-Chung Liew, Hong Yan, "Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images", Current Medical Imaging Reviews, Vol. 2, No. 1, 2006, 91-103.
6. Guttmann, C., Jolesz, F. A., Kikinis, R., Killiany, R., Moss, M., Sandor, T., and Albert, M, "White matter changes with normal aging", Neurology 1998, 50:972-978.
7. Heindel, W. C., Jernigan, T. L., Archibald, S. L., Achim, C. L., Masliah, E., and Wiley, C. A, "The relationship of quantitative brain magnetic resonance imaging measures to neuropathologic indexes of human immunodeficiency virus infection", Archives of Neurology, 1994; 51:1129-1135.
8. Niessen W., Vincken K., Weickert J., "Haar Romeny B., Viergever M.: Multiscale segmentation of threedimensional MR brain images", Internat. J. Comput, Vision 31 (2/3), (1999)185-202.
9. Suzuki H, Toriwaki J, "Automatic segmentation of head MRI images by knowledge guided thresholding", Computerized Medical Imaging and Graphics, 1991; 15(4): 233-240.
10. Singh M, Patel P, Khosla D, Kim T, "Segmentation of functional MRI by K-means clustering", IEEE Trans. Nucl, Sci. 1996; 43: 2030-2036.
11. Heinonen T, Dastidar P, Eskola H, Frey H, Ryymin P, Laasonen E, "Applicability of semi-automatic segmentation for volumetric analysis of brain lesions", Journal ofMedical Engineering & Technology 1998, 22: 173-178.
12. Bueno G, Musse O, Heitz F, Armspach JP, "3D Watershed-based segmentation of internal structures within MR brain images", Medical Images 2000: Image processing, Proc. SPIE, 2000; 3979: 284-293.
13. Bomans M, Hohne KH, Tiede U, Riemer M, "3D segmentation of MR images of the head for 3D display", IEEE Trans. Med. Imag, 1990; 9(2):177-183.
14. McInerney T, Terzopoulos D, "Deformable models in medical image analysis: A survey", Medical Image Analysis, 1996; 1(2):91-108.
15. Ji L, Yan H, "An attractable snakes based on the greedy algorithm for contour extraction", Pattern Recognition 2002, 35(4):791-806.
16. Thompson P, Toga AW, "Detection, visualization and animation of abnormal anatomic structure with a probabilistic brain atlas based on random vector field transformations", Med. Image Anal. 1997, 1:271-294.
17. Dawant BM, Hartmann SL, Thirion JP, Maes F, Vandermeulen D, Demaerel P, "Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and freeform transformations. I. Methodology and validation on normal subjects", IEEE Trans. Med. Imag, 1999; 18(10): 909-916
18. Karen Chia-Ren liu, Miin-Shen Yang, Hsiu-Chih Liu, Jiing-Feng Lirng and Pei-Ning Wang, "Generalized Kohonen's competitive learning algorithm for ophthalmological MR image segmentation", Magnetic Resonance Imaging, 21 (2003) 863-870.
19. Ozkan M, Dawant BM, Maciunas RJ, "Neural-network-based segmentation of multi-modal medical images: A comparative and prospective study", IEEE Trans Medical Imaging, 12(1993): 534-544.
20. Vincent L, Soille P, "Watersheds in digital spaces: An efficient algorithm based on immersion simulations", IEEE Trans. on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598.
21. J. Shi and J. Malik, "Normalized cuts and image egmentation", IEEE Trans. On Pattern Analysis and Machine Intelligence, 22(8) (2000) 888-905.
22. G.H. Golub and C.F. Van Loan, "Matrix Computations", John Hopkins Press, 1989.
23. V. Grau, A. Mewes, M. Alca?niz, R. Kikinis and S, "Warfield, Improved watershed transform for medical image segmentation using prior information", IEEE Transactions on Medical Image, 23(4) (2004)447-458.
24. A. Zijdenbos, B. Dawant, "Brain segmentation and white matter lesion detection in MR im-ages", Crit. Rev. Biomed. Eng, 22 (5-6), pp. 401-465, 1994.
25. Collins D.L., Zijdenbos A.P. and etal, "Design and Construction of a Realisticdigital Brain Phantom", IEEE Trans. Med. Imag, 17, (1998)463C468.
26. J. De Bock, P. De Smet, W. Philips, "Image segmentation using Watersheds and Normalized Cuts", SPIE Electronic Imaging 2005,Vision Geometry XIII (EI113)