Karyotyping Techniques of Chromosomes: A Survey

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-22 Number-1
Year of Publication : 2015
Authors : Revathy M Nair, Remya R S, Sabeena K


Revathy M Nair, Remya R S, Sabeena K "Karyotyping Techniques of Chromosomes: A Survey". International Journal of Computer Trends and Technology (IJCTT) V22(1):30-34, April 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Chromosome analysis is used by clinicians for the early detection, prognosis and treatment evaluation of genetic disorders like leukaemia, Down syndrome etc. The detection is done with the help of karyotypes. Karyotyping is a challenging and difficult task. So an automated system of karyotyping is needed for doing it in less time and with more accuracy. In literature various methods have been proposed for efficient and automated Karyotyping. But an accurate and more efficient computer assisted system needs further research and studies. In this paper we are making a survey of some of the methods for karyotyping in the literature proposed so far and critically reviewing them by listing the strengths and weakness of each of them.

[1] Weeks A r, Myler H R and Wenaas H G “ Computer generated noise images for the evaluation of image processing algorithms” Opt.Eng 32982-92, 1993
[2] Jain A K “Digital Image Processing”
[3] Anderson C H, Greenspan H, Akber S “Image Enhancement by nonlinear extrapolation in frequency space” IEEE Trans. Image Process.9 1035-48
[4] Gonzalez “Digital Image Processing”
[5] Wang Y, Wu Q, Castleman K R and Xiong Z “Chromosome image enhancement using multiscale differential operators” IEEE Trans. Med.Imag.22 685-93, 2003
[6] N Ostu, “A threshold selection method from grey level histogram” IEEE transactions on system man and cybernetics 9(1979) 62-66
[7] J Kapur, P Sahoo, A Wong “A new method for grey level picture thresholding using the entropy of the histogram” Computer graphics and image processing 29(3) (1985) 273- 285
[8] B.J.C, “Pattern recognition with fuzzy objective function algorithms” Plenum press, New York 1981
[9] F Yan, H Zhang, R Kube “A Multistage adaptive thresholding method” , Pattern recognition letters(2005) 1183- 1191.
[10] G Li, C Y Kao, J G Gore, Z Ding “Minimization of region scalable fitting energy for image segmentation” IEEE Transactions on Image processing 17(10) (2008) 1940-1949.
[11] J Zhang, Y Chen, X Huang “Edge detection of images based on improved Sobel operator and genetic algorithms”, International conference on Image analysis and signal processing ISAP09,China, 2009 pp. 31-35
[12]S Janani, R Nandakumar, M Nirmala “Feature extraction and pairing of G banded Chromosome image using K nearest neighbour classifier” IJCST vol 3, Issue 2 April-June 2012
[13] Schwartzkopf W, Evans B L and Bovik A C “Minimum entropy segmentation applied to multi spectral chromosome images” 2001 Int. Conf. on Image Processing Vol 2,pp 865-8
[14] Mousmi V Munot, Madhuri Joshi, Nikhil Sharma, Gulsheen Ahuja “Automated detection of cut points for disentangling overlapping chromosomes” 2013 IEEE point of care healthcare technologies, Bangalore, India 16-18 January 2013
[15] J Cho, S Y Ryu, S H Woo, “A study for the hierarchical artificial neural network model for Giemsa stained human chromosome classification” Proceedings of the 26th annual international conference of the IEEE EMBS Sept 1-5,2004
[16] Jim piper, Erik Granum, “On fully automatic feature measurement for banded chromosome classification” Cytometry 10:242-255(1989)
[17] Nirmala Madian, K B Jayanthi “Analysis of human chromosome classification using centromere position” Elsevier meas 47(2014) 287-295
[18] Artem khmelinskii, Rodrigo Venture, Joao Sanches, “A novel metric for bone marrow cells chromosome pairing” IEEE Transactions on biomedical engineering, vol 57 no.6,June 2010
[19] Mehdi Moradi, S Kamaledin Setarehdan, “New features for automatic classification of human chromosome: A feasibility study” Pattern recognition letters 27(2006) 19-28
[20] Ozy sjahputera, James M Keller, “Evolution of a fuzzy rule based system for automatic chromosome recognition”, 1999 IEEE International fuzzy systems conference proceedings Aug 22-25,1999
[21] Rodrigo Ventura, Artem Khmelinskii, J Miiguel Sanches “Classifier assisted metric for chromosome pairing” IEEE conf on Aug 31-sep 4, 2010
[22] Christoforos Markou, Christos Maramis, Anastasios Delopoulos “Automatic chromosome classification using support vector machines”.

Chromosome, Metaphase, Karyotyping, Medial Axis, Feature Extraction, Classification