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Reseach Article

An Efficient Segmentation Method for Overlapping Chromosome Images

by Tanvi, Renu Dhir
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 1
Year of Publication: 2014
Authors: Tanvi, Renu Dhir
10.5120/16560-4861

Tanvi, Renu Dhir . An Efficient Segmentation Method for Overlapping Chromosome Images. International Journal of Computer Applications. 95, 1 ( June 2014), 29-32. DOI=10.5120/16560-4861

@article{ 10.5120/16560-4861,
author = { Tanvi, Renu Dhir },
title = { An Efficient Segmentation Method for Overlapping Chromosome Images },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 1 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number1/16560-4861/ },
doi = { 10.5120/16560-4861 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:18:19.389893+05:30
%A Tanvi
%A Renu Dhir
%T An Efficient Segmentation Method for Overlapping Chromosome Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 1
%P 29-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Systems are being developed to automate the task of classification of chromosomes. The chromosomes are non-rigid material and they are several times touching each other or they overlap each other in the metaphase images. So different techniques are required to segregate the overlapping chromosomes. This paper presents a novel method for segmenting chromosomes based upon computational geometry. In the proposed approach first the contour line is traced for the overlapping chromosomes and then all the cut points are traced for the overlapping chromosomes. Then based on computational geometry method a specific number of cut points are selected and they are used for separating the two chromosomes. We have found that 87. 4% of the images were correctly segmented using the proposed method.

References
  1. A Carothers and J Piper. Computer-aided classification of human chromosomes: A review. Statist. Computing 1994; 4, 161-71.
  2. B Lerner. Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans. Syst. Man Cybern. B: Cybern. 1998; 28, 544-52.
  3. FCA Groen, TK Kate, AWM Smeulders and IT Young. Human chromosome classification based on local band descriptors. Patt. Recog. Lett. 1989; 9, 211-22.
  4. J Piper, E Granum, D Rutovitz and H Ruttledge. Automation of chromosome analysis. Sig. Proc. 1980; 2, 203-21.
  5. M Moradi and SK Setarehdan. New features for automatic classification of human chromosomes: A feasibility study. Patt. Recog. Lett. 2006; 27, 19-28.
  6. J Liang. Intelligent splitting in the chromosome domain. Patt. Recog. 1989a; 22, 519-32.
  7. J Liang. Decomposition of overlapping chromosomes. In: C Lundsteen and J Piper (ed). Automation of Cytogenetics, Springer, Berlin, 1989b, p. 177-90.
  8. J Liang. Fully automatic chromosome segmentation. Cytometry 1994; 17, 196-208.
  9. G Agam and I Dinstein. Geometric separation of partially overlapping nonrigid objects applied to automatic chromosome classification. IEEE Trans. Patt. Anal. Mach. Intell. 1997; 19, 1212-22.
  10. B Lerner, H Guterman and I Dinstein. A classification-driven partially occluded object segmentation (CPOOS) method with application to chromosome analysis. IEEE Trans. Sig. Proc. 1998; 46, 2841-47.
  11. M Popescu, P Gader, J Keller, C Klein, J Stanley and C Caldwell. Automatic karyotyping of metaphase cells with overlapping chromosomes. Comp. Biol. Med. 1999; 29, 61-82.
  12. GC Charters and J Graham. Trainable grey level models for disentangling overlapping chromosomes. Patt. Recog. 1999; 32, 1335-49.
  13. GC Charters and J Graham. J. Disentangling chromosome overlaps by combining trainable shape models with classification evidence. IEEE Trans. Sig. Proc. 2002; 50, 2080-85.
  14. R Ogniewicz and M Ilg. Voronoi skeletons: Theory and applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Illinois. 1992, p. 63-9.
  15. M Styner, G Gerig, J Lieberman, D Jones and D Weinberger. Statistical shape analysis of neuroanatomical structures based on medial models. Med. Image Anal. 2003; 7, 207-20.
  16. CM Boesse, MK Henry, TW Hyde and LS Matthews. Digital imaging and analysis of dusty plasmas. Adv. Space Res. 2004; 34, 2374-78.
  17. PK Sahoo, S Soltani and AKC Wong. A survey of thresholding techniques. Comp. Vis. Graph. Image Proc. 1988; 41, 233-60.
  18. RM Haralick and LG Shapiro. Computer and Robot Vision, Vol. I, Addison-Wesley, Reading, MA, 1992, p. 1-608.
  19. N Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979; 9, 62-6.
  20. S Wolfram. The Mathematica Book, 5th ed. , Book News, Oregon, 2004, p. 1-1200.
  21. H Freeman and LS Davis. A Corner Finding Algorithm for Chain Coded Curves. IEEE Trans. Comp. 1977; 26, 297-303.
  22. T Pavlidis. Algorithms for Shape Analysis of Contours and Waveforms. IEEE Trans. Patt. Anal. Mach. Intell. 1980; 2, 301-12.
  23. A Rosenfeld and AC Kak. Digital Picture Processing. Academic Press, California, 1982, p. 1-349.
  24. W Srisang, K Jaroensutasinee and M Jaroensutasinee. Segmentation of overlapping chromosome images. In: Abstracts of the 30th Congress on Science and Technology of Thailand, Bangkok, Thailand. 2004, p. 40.
  25. E. Grisan, E. Poletti, A. Ruggeri. Automatic segmentation and disentangling of chromosome in Q-band prometaphase images, IEEE Trans Inf Technol B, 2009.
  26. Boaz Lerner: Toward a completely automatic neural-network-based human chromosome analysis. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(4): 544-552 (1998)
  27. J. Graham, "Resolution of Composites in interactive karyotyping", in automation of cytogynetics,191-203, Springer-Verlag, Berlin,1989.
  28. L. Vanderheydt, F. Dom, A. Oosterlinck, and H. Van Den Berghe, "Two-Dimensional Shape Decomposition Using Fuzzy Subset Theory Applied to Automated Chromosome Analysis," Pattern Recognition, vol. 13, no. 2, pp. 147-157, 1981.
  29. A. M. Vossepoel, Analysis of Image Segmentation for Automated Chromosome Identification, University of Leiden, Leiden,Netherlands, Doctoral Dissertation, 1987.
  30. Q. Wu, Automated Identification of Human Chromosomes as an Exercise in Building Intelligent Image Recognition Systems, Catholic University of Leuven, Leuven, Belgium, Doctoral Dissertation, 1987.
  31. L. Ji, "Intelligent Splitting in the Chromosome Domain," Pattern Recognition, vol. 22, no. 5, pp. 519-532, 1989
  32. Lijiya, A, K. S. Sreejini, V. K. Govindan. 2012. M-FISH Chromosome Images Classification by Watershed based Segmentation Approach, 25 March,2012. International conference on advances in Computer,Electrical and Electronics Engineering(ICACEEE-2012),. , Bombay,India
Index Terms

Computer Science
Information Sciences

Keywords

Chromosome segmentation chromosome analysis overlapping chromosomes