single-jc.php

JACIII Vol.16 No.1 pp. 76-86
doi: 10.20965/jaciii.2012.p0076
(2012)

Paper:

Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation

Chastine Fatichah*,**, Martin Leonard Tangel*,
Muhammad Rahmat Widyanto***, Fangyan Dong*,
and Kaoru Hirota*

*Dept. Computational Intelligence & Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**Informatics Department, Faculty of Technology Information, Institut Teknologi Sepuluh Nopember, Kampus ITS Surabaya 60111 Indonesia

***Faculty of Computer Science, University of Indonesia, Kampus UI Depok, Jawa Barat, Indonesia

Received:
July 4, 2011
Accepted:
October 12, 2011
Published:
January 20, 2012
Keywords:
binary morphology, fuzzy morphology, white-blood-cell image, image segmentation, color ordering scheme
Abstract

An Interest-based Ordering Scheme (IOS) for fuzzy morphology on White-Blood-Cell (WBC) image segmentation is proposed to improve accuracy of segmentation. The proposed method shows a high accuracy in segmenting both high- and low-density nuclei. Further, its running time is low, so it can be used for real applications. To evaluate the performance of the proposed method, 100 WBC images and 10 leukemia images are used, and the experimental results show that the proposed IOS segments a nucleus in WBC images 3.99% more accurately on average than the Lexicographical Ordering Scheme (LOS) does and 5.29% more accurately on average than the combined Fuzzy Clustering and Binary Morphology (FCBM) method does. The proposal method segments a cytoplasm 20.72% more accurately on average than the FCBM method. The WBC image segmentation is a part of WBC classification in an automatic cancer-diagnosis application that is being developed. In addition, the proposed method can be used to segment any images that focus on the important color of an object of interest.

Cite this article as:
C. Fatichah, M. Tangel, <. Widyanto, F. Dong, and <. Hirota, “Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.1, pp. 76-86, 2012.
Data files:
References
  1. [1] N. Guo, L. Zeng, and Q. Wu, “A Method based on Multispectral Imaging Technique for White Blood Cell Segmentation,” Computers in Biology and Medicine, Vol.37, pp. 70-76, 2006.
  2. [2] S. Eom, S. Kim, V. Shin, and B. Ahn, “Leukocyte Segmentation in Blood Smear Images Using Region-Based Active Contours,” Lectures Notes in Computer Science, LNCS(4179), pp. 867-876, 2006.
  3. [3] L. B. Dorini, R. Minetto, and N. J. Leite, “White blood cell segmentation using morphological operators and scale-space analysis,” Brazilian Symposium on Computer Graphics & Image Processing (SIBGRAPI), pp. 294-304, 2007.
  4. [4] K. Jiang, Q. Liao, and S. Dai, “A Novel White Blood Cell Segmentation Scheme Using Scale-Space Filtering And Watershed Clustering,” Proc. of the Second Int. Conf. on Machine Learning and Cybernetics, Xian, pp. 2820-2825, 2003.
  5. [5] N. Theera-Umpon, “White Blood Cell Segmentation and Classification in Microscopic Bone Marrow Images,” Springer-Verlag Berlin Heidelberg, pp. 787-796, 2005.
  6. [6] L. Yang, P. Meer, and D. J. Foran, “Unsupervised Segmentation Based on Robust Estimation and Color Active Contour Models,” IEEE Trans. on Information Technology in Biomedicine, Vol.9, No.3, pp. 475-486, 2005.
  7. [7] F. Zamani and R. Safabakhsh, “An unsupervised GVF Snake Approach for White Blood Cell Segmentation based on Nucleus,” ICSP2006 Proc., 2006.
  8. [8] C. Pan, Y. Fang, X. Yan, and C. Zheng, “Robust Segmentation for Low Quality Cell Images from Blood and Bone Marrow,” Int. J. of Control, Automation, and Systems, Vol.4, No.5, pp. 637-644, 2006.
  9. [9] S. Colantonio, O. Salvetti, and I. B. Gurevich, “A two-step approach for automatic microscopic image segmentation using fuzzy clustering and Neural Discrimination,” Pattern Recognition and Image Analysis, Vol.17, No.3, pp. 428-437, Pleiades Publishing, Ltd., 2007.
  10. [10] B. Ko, M. Seo, and J. Nam, “Microscopic Cell Nuclei Segmentation Based on Adaptive Attention Window,” J. of Digital Imaging, Vol.22, No.3, pp. 259-274, Springer, 2009.
  11. [11] C. Reta, L. Altamirano, J. A. Gonzalez, R. Diaz, and J. S. Guichard, “Segmentation of Bone Marrow Cell Images for Morphological Classification of Acute Leukemia,” Proc. of the Twenty-Third Int. Florida Artificial Intelligence Research Society Conf. (FLAIRS), 2010.
  12. [12] T. Deng and H. Heijmans, “Grey-scale Morphology Based on Fuzzy Logic,” J. of Mathematical Imaging and Vision, Springer Netherlands, Vol.16, No.2, pp. 155-171, 2002.
  13. [13] A. Hanbury and J. Serra, “Mathematical Morphology in the HLS Colour Space,” Proc. of the 12th BMVC British Machine Vision Conf., Vol.II, pp. 451-460, 2001.
  14. [14] A. Ledda and W. Philips, “Majority ordering for colour mathematical morphology,” Proc. of the 13th European Signal Processing Conf. EUSIPCO2005, Antalya, Turkey, 2005.
  15. [15] R. M. Haralick, S. R. Stenberg, and X. Zhuang, “Image Analysis using Mathematical Morphology,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.PAMI-9, No.4, 1987.
  16. [16] D. Sinha and E. R. Dougherty, “Fuzzy Mathematical Morphology,” J. of Visual Communication and Image Representation, Vol.3, No.3, pp. 286-302, 1992.
  17. [17] I. Bloch and H. Maitre, “Fuzzy Mathematical Morphology,” Annals of Mathematics and Artificial Intelligence, Vol.10, pp. 55-84, 1994.
  18. [18] I. Bloch and H. Maitre, “Fuzzy Mathematical Morphologies: A comparative study,” Pattern Recognition, Vol.28, No.9, pp. 1341-1387, 1995.
  19. [19] B. DeBaets and E. Kerre, “The fundamentals of fuzzy mathematical morphology part 1: Basic concepts,” Int. J. of General Systems, Vol.23, pp. 155-171, 1995.
  20. [20] A. Bouchet, J. Pastore, and V. Ballarin, “Segmentation of Medical Images using Fuzzy Mathematical Morphology,” J. of Computer Science & Technology, Vol.7, No.3, pp. 256-262, 2007.
  21. [21] W. Chen, Y. Q. Shi, and G. Xuan, “Identifying computer graphics using HSV color model and statistical moments of characteristic functions,” IEEE Int. Conf. on Multimedia and Expo (ICME07), Beijing, China, July 2-5, 2007.
  22. [22] A. Asano, “Granulometry and skeleton,” Pattern Information Processing Session 9, December 2008.
  23. [23] http://www.cellavision.se/cellatlas

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Jan. 19, 2019