Automated segmentation of the melanocytes in skin histopathological images

IEEE Journal of Biomedical and Health Informatics

Volumn 17, Issue 2, 2013, Pages 284-296

  Lu, Cheng ^a   Mahmood, Muhammad ^a   Jha, Naresh ^a   Mandal, Mrinal ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Histopathological image analysis; Image segmentation; Local descriptor; Object detection; Pattern recognition

Indexed keywords

IMAGE ANALYSIS; OBJECT DETECTION; PATTERN RECOGNITION;

AUTOMATED SEGMENTATION; COMPUTER AIDED TECHNIQUE; HISTOPATHOLOGICAL IMAGE ANALYSIS; HISTOPATHOLOGICAL IMAGES; INITIAL SEGMENTATION; LOCAL DESCRIPTORS; MEAN SHIFT ALGORITHM; SEGMENTATION ALGORITHMS;

IMAGE SEGMENTATION;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; CELL NUCLEUS; CYTOLOGY; HUMAN; IMAGE PROCESSING; KERATINOCYTE; MELANOCYTE; PHYSIOLOGY; PROCEDURES; ARTICLE; METHODOLOGY;

ALGORITHMS; CELL NUCLEUS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KERATINOCYTES; MELANOCYTES; PATTERN RECOGNITION, AUTOMATED;

EID: 84885098320     PISSN: 21682194     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2012.2199595     Document Type: Article

References (25)

1. I. Maglogiannis and C. Doukas, "Overview of advanced computer vision systems for skin lesions characterization," IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 5, pp. 721-733, Sep. 2009.
2. A. C. Society, "What are the key statistics about melanoma?" Amer. Cancer Soc., Tech. Rep., 2008.
3. D. Rigel, J. Russak, and R. Friedman, "The evolution of melanoma diagnosis: 25 years beyond the ABCDs," CA: Canc. J. Clinic., vol. 60, no. 5, pp. 301-316, 2010.
4. J. Pawley, Handbook of Biological Confocal Microscopy. New York: Springer-Verlag, 2006.
5. V. Kumar, A. Abbas, and N. Fausto, Robbins and Cotran Pathologic Basis of Disease. Amsterdam, The Netherlands/Philadelphia, PA: Elsevier/Saunders, 2005.
6. S. Ismail, A. Colclough, J. Dinnen, D. Eakins, D. Evans, E. Gradwell, J. O'Sullivan, J. Summerell, and R. Newcombe, "Observer variation in histopathological diagnosis and grading of cervical intraepithelial neoplasia," Brit. Med. J., vol. 298, no. 6675, p. 707, 1989.
7. M. Gurcan, T. Pan, H. Shimada, and J. Saltz, "Image analysis for neuroblastoma classification: Segmentation of cell nuclei," in Proc. 28th IEEE Annu. Int. Conf. EMBS, 2006, pp. 4844-4847.
8. S. Petushi, F. U. Garcia, M. M. Haber, C. Katsinis, and A. Tozeren, "Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer," BMC Med. Imag., vol. 6, p. 14, 2006.
9. L. Coelho, A. Shariff, and R. Murphy, "Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms," in Proc. IEEE Int. Symp. Biomed. Imag.: Nano Macro, 2009, pp. 518-521.
10. L. E. Boucheron, "Object- and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer," Ph.D. dissertation, Univ. California, Santa Barbara, Mar. 2008.
11. X. Chen, X. Zhou, and S. Wong, "Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy," IEEE Trans. Biomed. Eng., vol. 53, no. 4, pp. 762-766, Apr. 2006.
12. T. Nattkemper, H. Ritter, and W. Schubert, "A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections," IEEE Trans. Inf. Technol. Biomed., vol. 5, no. 2, pp. 138-149, Jun. 2001. (Pubitemid 32609938)
13. S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, "Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology," in Proc. 5th IEEE Int. Symp. Biomed. Imag.: Nano Macro ISBI 2008, pp., 284-287.
14. O. Sertel, U. Catalyurek, H. Shimada, and M. Guican, "Computer-aided Prognosis of Neuroblastoma: Detection of mitosis and karyorrhexis cells in digitized histological images," in Proc. 31st IEEE Annu. Int. Conf. EMBS, 2009, pp. 1433-1436.
15. A. N. Basavanhally, S. Ganesan, S. Agner, J. P. Monaco, M. D. Feldman, J. E. Tomaszewski, G. Bhanot, and A. Madabhushi, "Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology," IEEE Trans. Biomed. Eng., vol. 57, no. 3, pp. 642-653, Mar. 2010.
16. V. Korde, H. Bartels, J. Barton, and J. Ranger-Moore, "Automatic segmentation of cell nuclei in bladder and skin tissue for karyometric analysis," Anal. Quant. Cytol. Histol., vol. 31, no. 2, p. 83, 2009.
17. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002. (Pubitemid 34550429)
18. J. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge, U.K.: Cambridge Univ. Press, 1999.
19. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst., Man, Cybern., vol. 9, no. 1, pp. 62-66, 1979. (Pubitemid 9413341)
20. M. Fitzgibbon, A. W. Pilu, and R. B. Fisher, "Direct least-squares fitting of ellipses," IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 5, pp. 476-480, 1999.
21. D. Weedon and G. Strutton, Skin Pathology. vol. 430, New York: Churchill Livingstone, 2002.
22. C. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Oxford Univ. Press, 1995.
23. C. Z. M. GmbH. (2009). "Mirax MIDI - the versatile slide scanner for research and routine," [Online]. Available: www.zeiss.de/mirax.
24. I. Sintorn, M. Homman-Loudiyi, C. Söderberg-Nauclér, and G. Borgefors, "A refined circular template matching method for classification of human cytomegalovirus capsids in tem images," Comput. Methods Programs Biomed., vol. 76, no. 2, pp. 95-102, 2004. (Pubitemid 39286703)
25. J. Lewis, "Fast normalized cross-correlation," in Proc. Conf. Vision Interface, 1995, vol. 10, pp. 120-123.