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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 592790, 10 pages
An Entropy-Based Automated Cell Nuclei Segmentation and Quantification: Application in Analysis of Wound Healing Process
1Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
2Department of Biochemistry & Molecular Biology, Virginia Commonwealth University, Richmond, VA 23298, USA
Received 23 October 2012; Revised 22 January 2013; Accepted 26 January 2013
Academic Editor: Tianhai Tian
Copyright © 2013 Varun Oswal 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.
- S. Y. Ji, R. Smith, T. Huynh, and K. Najarian, “A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries,” BMC Medical Informatics and Decision Making, vol. 9, no. 2, 2009.
- E. Bak, K. Najarian, and J. P. Brockway, “Efficient segmentation framework of cell images in noise environments,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBCS '04), pp. 1802–1805, September 2004.
- S. Di Cataldo, E. Ficarra, A. Acquaviva, and E. Macii, “Automated segmentation of tissue images for computerized IHC analysis,” Computer Methods and Programs in Biomedicine, vol. 100, no. 1, pp. 1–15, 2010.
- J. C. Sieren, J. Weydert, A. Bell et al., “An automated segmentation approach for highlighting the histological complexity of human lung cancer,” Annals of Biomedical Engineering, vol. 38, no. 12, pp. 3581–3591, 2010.
- S. Wienert, D. Heim, K. Saeger et al., “Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach,” Scientific Reports, vol. 2, p. 503, 2012.
- C. W. Wang, “Fast automatic quantitative cell replication with fluorescent live cell imaging,” BMC Bioinformatics, vol. 13, p. 21, 2012.
- P. R. Gudla, K. Nandy, J. Collins, K. J. Meaburn, T. Misteli, and S. J. Lockett, “A high-throughput system for segmenting nuclei using multiscale techniques,” Cytometry A, vol. 73, no. 5, pp. 451–466, 2008.
- Y. Al-Kofahi, W. Lassoued, W. Lee, and B. Roysam, “Improved automatic detection and segmentation of cell nuclei in histopathology images,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 4, pp. 841–852, 2010.
- T. Markiewicz, C. Jochymski, R. Koktysz, and W. Kozlowski, “Automatic cell recognition in immunohistochemical gastritis stains using sequential thresholding and SVM network,” in Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '08), pp. 971–974, May 2008.
- B. Ko, M. Seo, and J. Y. Nam, “Microscopic cell nuclei segmentation based on adaptive attention window,” Journal of Digital Imaging, vol. 22, no. 3, pp. 259–274, 2009.
- V. R. Korde, H. Bartels, J. Barton, and J. Ranger-Moore, “Automatic segmentation of cell nuclei in bladder and skin tissue for karyometric analysis,” Analytical and Quantitative Cytology and Histology, vol. 31, no. 2, pp. 83–89, 2009.
- X. Zhou, F. Li, J. Yan, and S. T. C. Wong, “A novel cell segmentation method and cell phase identification using Markov model,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 152–157, 2009.
- N. Malpica, C. O. de Solórzano, J. J. Vaquero et al., “Applying watershed algorithms to the segmentation of clustered nuclei,” Cytometry A, vol. 28, no. 4, pp. 289–297, 1997.
- S. Singh, “Cancer cells detection and classification in biopsy image,” International Journal of Computer Applications, vol. 38, no. 3, pp. 15–21, 2012.
- S. Singh, P. R. Gupta, and M. K. Sharma, “Breast cancer detection and classification of histopathological images,” International Journal of Engineering Science and Technology, vol. 3, no. 5, p. 4228, 2010.
- H. F. Dvorak, “Tumors: wounds that do not heal. Similarities between tumor stroma generation and wound healing,” New England Journal of Medicine, vol. 315, no. 26, pp. 1650–1659, 1986.
- S. M. Alaish, D. A. Bettinger, O. O. Olutoye et al., “Comparison of the polyvinyl alcohol sponge and expanded polytetrafluoroethylene subcutaneous implants as models to evaluate wound healing potential in human beings,” Wound Repair and Regeneration, vol. 3, no. 3, pp. 292–298, 1995.
- R. F. Diegelmann, W. J. Lindblad, and I. K. Cohen, “A subcutaneous implant for wound healing studies in humans,” Journal of Surgical Research, vol. 40, no. 3, pp. 229–237, 1986.
- L. N. Jorgensen, L. Olsen, F. Kallehave, et al., “The wound healing process in surgical patients evaluated by the expanded polytetrafluoroethylene and the polyvinyl alcohol sponge: a comparison with special reference to intrapatient variability,” Wound Repair and Regeneration, vol. 3, no. 4, pp. 527–532, 1995.
- Z. Li and K. Najarian, “Biomedical image segmentation based on shape stability,” in Proceedings of the 14th IEEE International Conference on Image Processing (ICIP '07), vol. 6, pp. 281–284, September 2007.
- F. J. Cisneros, P. Cordero, A. Figueroa, and J. Castellanos, “Histology image segmentation,” International Journal of Information Technology and Management, vol. 5, no. 1, pp. 67–76, 2011.
- N. Mirshahi, S. U. Demir, K. Ward, R. Hobson, R. Hakimzadeh, and K. Najarian, “An adaptive entropic thresholding technique for image processing and diagnostic analysis of microcirculation videos,” International Journal On Advances in Life Sciences, vol. 2, no. 3-4, pp. 133–142, 2011.
- K. Najarian and R. Splinter, Biomedical Signal and Image Processing, CRC Press, Florida, Fla, USA, 2nd edition, 2012.
- C. Wilson, D. Brown, K. Najarian, E. N. Hanley, and H. E. Gruber, “Computer aided vertebral visualization and analysis: a methodology using the sand rat, a small animal model of disc degeneration,” BMC Musculoskeletal Disorders, vol. 4, no. 1, p. 4, 2003.
- Y. Xiao, Z. Cao, and S. Zhong, “New entropic thresholding approach using gray-level spatial correlation histogram,” Optical Engineering, vol. 49, no. 12, Article ID 127007, 2010.
- N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
- Bio-Image Semantic Query User Environment (BISQUE), “Dataset of images,” http://bisque.ece.ucsb.edu/client_service/browser?resource=/data_service/dataset.