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Reseach Article

M-FISH Image Segmentation and Classification using Fuzzy Logic

by Lijiya A, Sreejithlal G S, Govindan V K
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 25
Year of Publication: 2013
Authors: Lijiya A, Sreejithlal G S, Govindan V K
10.5120/12227-8519

Lijiya A, Sreejithlal G S, Govindan V K . M-FISH Image Segmentation and Classification using Fuzzy Logic. International Journal of Computer Applications. 70, 25 ( May 2013), 46-51. DOI=10.5120/12227-8519

@article{ 10.5120/12227-8519,
author = { Lijiya A, Sreejithlal G S, Govindan V K },
title = { M-FISH Image Segmentation and Classification using Fuzzy Logic },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 25 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 46-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number25/12227-8519/ },
doi = { 10.5120/12227-8519 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:51.508713+05:30
%A Lijiya A
%A Sreejithlal G S
%A Govindan V K
%T M-FISH Image Segmentation and Classification using Fuzzy Logic
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 25
%P 46-51
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Karyotyping has an important role in identifying genetic disorders due to structural changes in chromosomes. Multiplex fluorescence in-situ hybridization (M-FISH) technique provides more precise karyotyping. The new classification method, proposed in this paper, automates karyotyping, based on Fuzzy c-means (FCM) algorithm combined with a labeling chart. Classification results show that the proposed method improves accuracy and running time. It is also observed that the accuracy of classification can further be improved, using a new Reclassification algorithm which reduces the chance of wrongly classified chromosome pixels.

References
  1. T. Ried, A. Baldini, T. C. Rand, and D. C. Ward. "Simultaneous visualization of seven different DNA probes by in situ hybridization using combinatorial fluorescence and digital imaging microscopy", Proc Natl Acad Sci U S A, 89(4):1388 – 1392, 1992.
  2. M. R. Speicher, S. Gwyn Ballard, and D. C. Ward. "Karyotyping human chromosomes by combinatorial multi-fluor FISH", Nature genetics, Vol. 12, No. 4. (April 1996), pp. 368-375.
  3. M. P. Sampat, K. R. Castleman, and A. C. Bovik. "Pixel-by-pixel classification of MFISH images", In Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint, volume 2, pages 999 – 1000, 2002.
  4. Yu-Ping Wang. "M-FISH image registration and classification", In Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on, pages 57 – 60, April 2004.
  5. Hyohoon Choi, K. R. Castleman, and A. C. Bovik. "Joint segmentation and classification of M-FISH chromosome images", In Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE, volume 1, pages 1636 – 1639, September 2004.
  6. P. S. Karvelis, D. I. Fotiadis, A. Tzallas, and I. Georgiou. "Region Based Segmentation and Classification of Multispectral Chromosome Images", In Computer-Based Medical Systems, 2007. CBMS '07. Twentieth IEEE International Symposium on, pages 251 – 256, June 2007.
  7. P. S. Karvelis and D. I. Fotiadis. "A region based decorrelation stretching method: Application to multispectral chromosome image classification", In Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on, pages 1456 – 1459, October 2008.
  8. Sreejini K S, Lijiya A, V K Govindan, "M-FISH Karyotyping – A New Approach Based on Watershed Transform," International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), vol. 2, pp. 105-117, 2012.
  9. Yu-Ping Wang; Dandpat, A. K. , "Classification of multi-spectral florescence in situ hybridization images with fuzzy clustering and multiscale feature selection", Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on , vol. , no. , pp. 95,96, 28-30 May 2006.
  10. Hongbao Cao; Hong-Wen Deng; Li, M. ; Yu-Ping Wang, "Classification of Multicolor Fluorescence In Situ Hybridization (M-FISH) Images With Sparse Representation", NanoBioscience, IEEE Transactions on, vol. 11, no. 2, pp. 111,118, June 2012
  11. Jingyao Li; Dongdong Lin; Hongbao Cao; Yu-Ping Wang, "Classification of multicolor fluorescence in-situ hybridization (M-FISH) image using structure based sparse representation model", Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on , vol. , no. , pp. 1,6, 4-7 Oct. 2012.
  12. A. Fazel, R. Derakhshani, and Yu Ping Wang. "Classification of multicolor fluorescence in situ hybridization images using gaussian mixture models", 2006.
  13. M. P. Sampat, A. C. Bovik, J. K. Aggarwal, and K. R. Castleman. "Supervised parametric and non-parametric classification of chromosome images", Pattern Recogn. 38, 8 (August 2005), 1209-1223.
  14. P. Karvelis, A. Likas, and D. I. Fotiadis. "Semi unsupervised M-FISH chromosome image classification", In Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on, pages 1 – 4, November 2010.
  15. Y. -P. Wang and Ashok Kumar Dandpat. "Classification of M-FISH images using fuzzy C-means clustering algorithm and normalization approaches", In Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on, volume 1, pages 41 – 44, November 2004.
  16. Hongbao Cao and Yu-Ping Wang. "Segmentation of M-FISH Images for improved classification of chromosomes with an adaptive fuzzy c-means clustering algorithm", In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pages 1442 – 1445, 30 2011-april 2 2011.
  17. D. L. Pham and J. L. Prince, "An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities," Pattern Recog. Lett. , vol. 20, pp. 57–68, 1998.
  18. D. L. Pham and J. L. Prince, "Adaptive fuzzy segmentation of magnetic resonance images," IEEE Trans. Med. Imag. , vol. 18, no. 9, pp. 737–752, Sep. 1999.
  19. Lijiya A, Sangeetha M. K. , and V. K. Govindan. "Segmentation and Classification of M-FISH Human Chromosome Images", In The second International Conference on Advances in Computing and Communications (ACC-2012), August 2012.
  20. Lijiya A, Sreejithlal G S, and Govindan V K. "M-FISH Image Segmentation Using Fuzzy Logic and Spatial Information", International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 2, No. 6,Pages 1249{1253, December 2012.
  21. J. C. Dunn. "Some recent investigations of a new fuzzy partition algorithm and its application to pattern classification problems", In Cybernetics, pages 1 – 15, 1974.
  22. J. C. Bezdek. "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum, New York, 1981.
  23. The ADIR M FISH Image Database. "http://www. adires. com/05/Project/MFISHDB/MFISHDB. shtml", Accessed: 21-Jul-2012.
Index Terms

Computer Science
Information Sciences

Keywords

Karyotyping Multiplex fluorescence in-situ hybridization (M-FISH) Fuzzy c-means (FCM) Labeling chart Reclassification