A new approach to white blood cell nucleus segmentation based on gram-schmidt orthogonalization

Proceedings - 2009 International Conference on Digital Image Processing, ICDIP 2009

Volumn , Issue , 2009, Pages 107-111

  Rezatofighi, S H ^a   Soltanian Zadeh, H ^a,b   Sharifian, R ^c   Zoroofi, R A ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

^b HENRY FORD HOSPITAL   (United States)

^c TEHRAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Gram schmidt process; Hematological image; Nucleus segmentation; Orthogonality; White blood cell

Indexed keywords

GRAM-SCHMIDT PROCESS; HEMATOLOGICAL IMAGE; NUCLEUS SEGMENTATION; ORTHOGONALITY; WHITE BLOOD CELL;

BIOINFORMATICS; BLOOD; CYTOLOGY; DIAGNOSIS; DIGITAL IMAGE STORAGE; IMAGE SEGMENTATION; IMAGING SYSTEMS; LINEAR ALGEBRA;

CELLS;

EID: 70449420120     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDIP.2009.19     Document Type: Conference Paper

References (21)

1. D. M. U. Sabino, L. d. F. Costa, E. G. Rizzatti, and M. A. Zago, "A texture approach to leukocyte recognition," Real-Time Imaging, vol. 10, pp. 205-216, 2004.
2. J. WU, P. Zeng, Y. Zhou, and C. Oliver, "A novel color image segmentation method and its application to white blood cell image analysis," ICSP, 2006.
3. H. Hengen, S.L. Spoor, and M.C. Pandit, "Analysis of blood and bone marrow smears using digital image processing techniques, " in: M. Sonka, J.M. Fitzpatrick, (Eds.), Proceedings of the SPIE, Medical Imaging 2002: Image Processing, vol. 4684, pp. 624-635, 2002.
4. I. Cseke, "A fast segmentation scheme for WBC images," IEEE Conf. Image, Speech and Signal Analysis, pp.530-533, V. CONCLUSION 1992.
5. S. F. Bikhet, A. M. Darwish, H. A. Tolba, and S. I. Shaheen, "Segmentation and clustering of WBC," IEEE Conf. Acoustics, Speech, and Signal Processing, vol. 4, pp. 2259-2261, 2000.
6. Q. Liano and Y. Deng, "An accurate segmentation method for white blood cell images," IEEE Conf. Biomedical Imaging, pp. 245-248, 2002.
7. J. Angulo and G. Flandrin, "Automated detection of working area of peripheral blood smears using mathematical morphology," Analytical Cellular Pathology, vol. 25, pp. 37-49, 2003.
8. M. Trivedi and J. C. Bezedek, "Low-level segmentation of acrial images with fuzzy clustering," IEEE Trans. On System, Man. And Cybernetics, vol. 16, no 4, pp. 589-598, 1986.
9. J. M. chassery and C. Garbay, "An iterative segmentation method based on contextual color and shape criterion," IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 795-800, 1984.
10. N. T. Umpon," Patch-based white blood cell nucleus segmentation using fuzzy clustering, "ECTI Trans. Electrical. Electronic. Communications, vol. 3, no. 1, pp. 5-10, 2005.
11. N. Guo, L. Zeng, and Q. Wu, "A method based on multi-spectral imaging technique for white blood cell segmentation," Computers in Biology and Medicine, vol. 37, pp. 70-76, 2006.
12. K. Jiang, Q. M. Liao, and S. Y. Dai, "A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering," Proceedings of the Second International Conference on Machine Learning and Cybernetics, pp. 2820-2825, 2003.
13. F. Zamani and R. Safabakhsh, "An unsupervised GVF snake approach for white blood cell segmentation based on nucleus," ICSP, 2006.
14. J. Park and J. M. Keller, "Snakes on the watershed," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, No. 10, 2001.
15. B. Ravi Kumar, D. K Joseph and T. V. Sreenivas, "Teager energy based blood cell segmentation," IEEE Conf. Digital Signal Processing, vol. 2, pp. 619-622, 2002.
16. C. Ruberto, A. Dempster, S. Khan, and B. Jarra, "Analysis of infected blood cell images using morphological operators," Image and Vision Computing, vol. 20, pp. 133-146, 2002.
17. H. Sheikh, B. Zhu, and E. Micheli, "Blood cell identification using neural networks," IEEE Conf. Biomedical Imaging, pp. 245-248, 1996.
18. J. Park and J. Keller, "Fuzzy patch label relaxation in bone marrow cell segmentation," Proc. IEEE Int'l Conf. Systems, Man. and Cybernetics, pp. 1133-1138, Oct. 1997.
19. B. Nilsson, A. Heyden, A. B. Cellavision and S. Lund, "Model-based segmentation of leukocytes clusters," IEEE Conf. of Pattern Recognition, vol. 2, pp. 727-730, 2002.
20. H. Soltanian-Zadeh, D. J. Peck, D. O. Hearshen, and R. R. Lajiness-O'Neill, "Model independent method for fMRI analysis," IEEE Trans. Med. Imag., vol. 23,no. 3, 2004.
21. E. Montseny, P. Sobrevilla, and S. Romani, "A fuzzy approach to white blood cells segmentation in color bone marrow images," IEEE Int. Conf. on Fuzzy Systems, pp. 173-178, 2004.