- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Submit a Manuscript
- Table of Contents
ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 923946, 12 pages
Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering
Department of Electrical Engineering, National Institute of Technology Rourkela, Orissa, Rourkela 769008, India
Received 6 October 2011; Accepted 21 November 2011
Academic Editor: C. Chen
Copyright © 2012 Subrajeet Mohapatra et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- H. Theml, H. Diem, and T. Haferlach, Color Atlas of Hematology, Thieme, 2004.
- D. Burnett and J. Crocker, The Science of Laboratory Diagnosis, John Wiley & Sons, New York, NY, USA, 2005.
- C. D. Tkachuk and J. V. Hirschmann, Wintrobe's Atlas of Clinical Hematology, Lippincott Williams & Wilkins, Philadelphia, Pa, USA, 1st edition, 2007.
- N. Satake and J. M. Yoon, “Acute lymphoblastic leukemia,” 2010, http://emedicine.medscape.com/.
- M. Ghosh, D. Das, C. Chakraborty, and A. K. Ray, “Automated leukocyte recognition using fuzzy divergence,” Micron, vol. 41, no. 7, pp. 840–846, 2010.
- N. T. Umpon, “Patch based white blood cell nucleus segmentation using fuzzy clustering,” ECTI Transaction Electrical Electronics Communications, vol. 3, no. 1, pp. 5–10, 2005.
- W. Shitong and W. Min, “A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 5–10, 2006.
- E. Montseny, P. Sobrevilla, and S. Romani, “A fuzzy approach to white blood cells segmentation in color bone marrow images,” in Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 1, pp. 173–178, 2004.
- N. Theera-Umpon and S. Dhompongsa, “Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification,” IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 3, pp. 353–359, 2007.
- A. I. Shihab, Fuzzy clustering algorithms and their application to medical image analysis, Ph.D. thesis, University of London, 2000.
- K. I. Laws, Texture image segmentation, Ph.D. thesis, University of South California, 1980.
- R. Adollah, M. Mashor, N. M. Nasir, H. Rosline, H. Mahsin, and H. Adilah, “Blood cell image segmentation: a review,” in Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering, N. A. Osman, F. Ibrahim, W. W. Abas, H. A. RahmanTing, and H. Ting, Eds., vol. 21, pp. 141–144, Springer, 2008.
- C. Di Rubeto, A. Dempster, S. Khan, and B. Jarra, “Segmentation of blood images using morphological operators,” in Proceedings of the 15th International Conference on Pattern Recognition, vol. 3, pp. 397–400, 2000.
- D. Anoraganingrum, “Cell segmentation with median filter and mathematical morphology operation,” in Proceedings of the International Conference on Image Analysis and Processing, pp. 1043–1046, 1999.
- J. Wu, P. Zeng, Y. Zhou, and C. Olivier, “A novel color image segmentation method and its application to white blood cell image analysis,” in Proceedings of the 8th International Conference on Signal Processing, vol. 2, 2006.
- T. Mouroutis, S. J. Roberts, and A. A. Bharath, “Robust cell nuclei segmentation using statistical modelling,” Bioimaging, vol. 6, no. 2, pp. 79–91, 1998.
- H. S. Wu, J. Barba, and J. Gil, “Iterative thresholding for segmentation of cells from noisy images,” Journal of Microscopy, vol. 197, no. 3, pp. 296–304, 2000.
- G. Lin, U. Adiga, K. Olson, J. F. Guzowski, C. A. Barnes, and B. Roysam, “A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks,” Cytometry Part A, vol. 56, no. 1, pp. 23–36, 2003.
- M. Ghosh, D. Das, S. Mandal et al., “Statistical pattern analysis of white blood cell nuclei morphometry,” in Proceedings of the IEEE Students' Technology Symposium (TechSym '10), pp. 59–66, April 2010.
- A. Mehnert and P. Jackway, “An improved seeded region growing algorithm,” Pattern Recognition Letters, vol. 18, no. 10, pp. 1065–1071, 1997.
- Q. Liao and Y. Deng, “An accurate segmentation method for white blood cell images,” in Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 245–258, 2002.
- A. Sinha and A. Ramakrishnan, “Automation of differential blood count,” in Proceedings of the Conference on Convergent Technologies for Asia-Pacific Region, pp. 547–551, 547–551, 2003.
- D. Comaniciu and P. Meer, Cell Image Segmentation for Diagnostic Pathology, Springer, New York, NY, USA, 2001.
- M. Kass, A. Witkins, and D. Terzopoulos, “Snakes: active contour models,” in Proceedings of the 1st International Conference on Computer Vision, pp. 259–268, 1987.
- G. Ongun, U. Halici, K. Leblebicioǧlu, V. Atalay, S. Beksac, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Analysis, Theory, Methods and Applications, vol. 47, no. 9, pp. 5839–5847, 2001.
- C. Di Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image and Vision Computing, vol. 20, no. 2, pp. 133–146, 2002.
- G. Ongun, U. Halici, K. Leblebicioglu, V. Atalay, M. Beksac, and S. Beksak, “A modified fuzzy clustering for white blood cell segmentation,” in Proceedings of the 3rd International Symposium on Biomedical Engineering, pp. 356–359, 2008.
- K. Jiang, Qing-Min, and S.-Y. Dai, “Red blood cell segmentation scheme utilizing various image segmentation techniques,” in Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, 2003.
- B. R. Kumar, D. K. Joseph, and T. Sreenivas, “Teager energy based blood cell segmentation,” in Proceedings of the 14th International Conference on Digital Signal Processing, Bangalore, India, 2002.
- S. Mohapatra and D. Patra, “Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images,” in Proceedings of the International Conference on Systems in Medicine and Biology (ICSMB '10), pp. 49–54, 2010.
- J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 1981.
- P. Lingras and C. West, “Interval set clustering of web users with rough K-means,” Journal of Intelligent Information Systems, vol. 23, no. 1, pp. 5–16, 2004.
- A. K. Jain, Fundamentals of Digital Image Processing, Pearson Education, India, 1st edition, 2003.
- S. Mohapatra, Deveopmant of impulse noise detection schemes for selective filtering, M.S. thesis, National Institute of Technolgy Rourkela, 2008.
- G. Gan, C. Ma, and J. Wu, Data Clustering Theory, Algorithms,and Applications, Society for Industrial and Applied Mathematics, 2007.
- N. K. Verma, A. Roy, and S. Vasikarla, “Medical image segmentation using improved mountain clustering technique version-2,” in Proceedings of the IEEE 7th International Conference on Information Technology, pp. 156–161, 2010.
- B. Clarke, E. Fokue, and H. H. Zhang, Principles and Theory for Data Mining and Machine Learning, Springer, New York, NY, USA, 2009.
- S. Mitra, “An evolutionary rough partitive clustering,” Pattern Recognition Letters, vol. 25, no. 12, pp. 1439–1449, 2004.
- S. Mitra, H. Banka, and W. Pedrycz, “Rough-fuzzy collaborative clustering,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 36, no. 4, pp. 795–805, 2006.
- B. Ristevski, S. Loshkovska, S. Dzeroski, and I. Slavkov, “A comparison of validation indices for evaluation of clustering results of dna microarray data,” in Proceedings of the The 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08), pp. 587–591, 2008.
- K. L. Wu, “An analysis of robustness of partition coefficient index,” in Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence) (FUZZ-IEEE '08), pp. 372–376, 2008.
- K.-L. Wu and M.-S. Yang, “A cluster validity index for fuzzy clustering,” Pattern Recognition Letters, vol. 26, no. 9, pp. 1275–1291, 2005.