CFP last date
20 January 2025
Reseach Article

Automated Karyotyping of Metaphase Cells with Touching Chromosomes

by Mousami V. Munot, Dr. Madhuri A. Joshi, Nikhil Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 12
Year of Publication: 2011
Authors: Mousami V. Munot, Dr. Madhuri A. Joshi, Nikhil Sharma
10.5120/3700-5175

Mousami V. Munot, Dr. Madhuri A. Joshi, Nikhil Sharma . Automated Karyotyping of Metaphase Cells with Touching Chromosomes. International Journal of Computer Applications. 29, 12 ( September 2011), 14-20. DOI=10.5120/3700-5175

@article{ 10.5120/3700-5175,
author = { Mousami V. Munot, Dr. Madhuri A. Joshi, Nikhil Sharma },
title = { Automated Karyotyping of Metaphase Cells with Touching Chromosomes },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 12 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number12/3700-5175/ },
doi = { 10.5120/3700-5175 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:37.398109+05:30
%A Mousami V. Munot
%A Dr. Madhuri A. Joshi
%A Nikhil Sharma
%T Automated Karyotyping of Metaphase Cells with Touching Chromosomes
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 12
%P 14-20
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Since the birth of the automated karyotyping systems by the aid of computers, building a fully automated chromosome analysis system has been an ultimate goal. Along with many other challenges, automating chromosome classification and segmentation has been a major challenge especially due to overlapping and touching chromosomes. The earlier reported methods have limited success as they are sensitive to scale variations, computationally complex, use only color information in case of multispectral imaging and had challenges in segmentation. The proposed technique addresses the challenge of separating the touching chromosomes using initially the modified snake algorithm to disentangle the cluster of touching chromosomes from the metaphase image and then a greedy approach based on combinatorial computational geometry of the pixels on the boundary of the cluster is used to identify and resolve the set of touching chromosomes. Contribution and novelty of this work lies in the ability of the algorithm to successfully separate the clusters of any number of touching chromosomes. System performance was tested and analyzed using a variety of metaphase images exhibiting various levels of touching chromosomes giving an overall accuracy of 100 % for resolving the cluster with 2 touching chromosomes and 95 % for separating a cluster of 3, 4 touches. The overall time was 2.4 seconds.

References
  1. Standing Committee on human Cyotgenetic Nomenclature, ISCN: an international system for human cytogentic nomenclature. Karger and cytogentics and cell Genetics, 1995.
  2. E. Grisan, E. Poletti, C Tomelleri, A Ruggeri, " Automatic Segmentation of Chromosomes in Q Band images", Proceddings of 29th annual International Conference of the IEEE EMBS , France, August 2007, pp 5513-5516.
  3. F. Cloopet, A. Boucher, " Segmentation of overlapping / aggregating nuclei cells in biological images" 2008
  4. W. Srisang, K, Jaroensutasinee, M. Jaroensutasinee, "Segmentation of overlapping chromosomes images using computational Geometry," Walailak Journal of Science and Technology, 2006, pp. 181-194.
  5. B. Lerner, " Toward A completely automatic neural network based human chromosome analysis", IEEE Transactions on system, Man, cybernetics-part B: cybernetics, vol. 28, no:4, August 1998, pp. 544-552.
  6. B. Lerner, H Guterman, I. Dinstein, " A classification driven partially occluded object segmentation (CPOOS) Method with application to chromosome analysis", IEEE Transactions on signal Processing, vol. 46, no. 10, October 1998 , pp 2841-2847.
  7. G. Agam, I. Dinstein, " Geometric Separation of Partially Overlapping Non rigid Objects applied to Automatic Chromosome Classification" , IEEE Transactions on pattern analysis and machine Intelligence, vol.19. no 11, November 1997.
  8. G. Charters, J Grahman, " Disentangling Chromosome overlaps by combining Trainable Shape models with Classification evidence" IEEE Transactions on signal Processing , vol. 50. no.8. August 2002.
  9. W. Schwwartzkopf, B. Evans, A. Bovik, "Minimum entropy Segmentation Applied to Multispectral Chromosome Images", pp 865-868.
  10. W. Schwwartzkopf, B. Evans, A. Bovik," Entropy Estimation for segmentation of Multi Spectral Chromosome Images", Fifth IEEE Southwest symposium on image analysis and interpretation ( SSIAI 02), 2002, pp-
  11. H. Choi, A. Bovik, K. Castleman, " Maximum Likelihood Decomposition of Overlapping and Touching MFISH chromosomes using Geometry, size and color information", Proceedings of the 28th IEEE EMBS Annual International Conference, USA, 2006
  12. W. Schwartzkopf, A. C. Bovik, B. Evans, " Maximum Likelihood Techniques for joint Segmentation - classification of Multispectral chromosome Images, " IEEE Transactions on Medical Imaging, vol 24, No. 12, December 2005.
  13. L. Ji, "Intelligent Splitting in the chromosome domain," Pattern Recognition, vol. 22, pp 519-532, 1989
  14. L. Ji, "Fully Automatic Chromosome Separation," Cytometry, vol. 17.pp 196-208,1994
  15. M. Ppoescu, P. Gader, J. Keller, C. Klein, J. Stanley, C. Caldweli, "Automatic Karyotyping of Metaphase cells with overlapping chromosomes," Computers in Biology and Medicine , vol.29,pp.61-82,1999
  16. A. Carothers, J Piper, “Computer Aided Classification of Human Chromosomes: A review,” Statistics and Computing, vol. 4, pp. 161-171,1994
  17. B. Lerner, “Toward a Completely Automatic Neural Network Based Human Chromosome Analysis,” IEEE transactions systems, man, and cybernetics-part B: cybernetics, vol. 28, no. 4, pp. 544-552, 1998.
  18. J. Piper, E. Granum, “On Fully Automatic Feature Measurement for Banded Chromosome Classification,” cytometry vol. 10, 1989, pp. 242- 255.
  19. A Khemlinskii, R. Ventura, J. Sanches, “ A Novel Metric for Bone Marrow Cells Chromosome Pairing,” IEEE Transactions on biomedical Engineering, Vol. 57, no. 6, June 2010.
  20. T. Cormen, C. Leiserson, R. Rivest, C. Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN 0-262-03293-7. Pages 957–961 of section 33.4:
  21. Paul E. Black, Dictionary of Algorithms and Data Structure- Manhattan distances, NIST
  22. Nikolas Tiilikainen, “A Comparative Study of Active Contour Snakes” 2007
Index Terms

Computer Science
Information Sciences

Keywords

Karyotyping Metaphase Combinatorial Computational Geometry Snake algorithm