Abstract

Genetic algorithm based Fuzzy C Mean (GAFCM) technique is used to segment spots of complimentary DNA (c-DNA) microarray images for finding gene expression is proposed in this paper. To evaluate the performance of the algorithm, simulated microarray slides were generated whose actual mean values were known and is used for testing. K-means, Fuzzy C Means (FCM) and the proposed GAFCM algorithm were applied to the simulated images for the separation of the foreground (FG) spot signal information from background (BG) and the results were compared. The strength of the algorithm was tested by evaluating the segmentation matching factor, coefficient of determination, concordance correlation and gene expression values. From the results it is observed that the segmentation ability of GAFCM is better compared to FCM and K- Means algorithms.

References

• Y. H. Yang, M. J. Buckley, S. Duboit, and T. P. Speed (2002), "Comparison of methods for image analysis on c- DNA microarray data," J. Comput. Graphical Statist. , vol. 11, pp. 108–136
• M. B. Eisen. (1999). ScanAlyze [Online]. Available:- http://rana. lbl. gov/ EisenSoftware. htm
• GenPix 4000, A User's Guide (1999), Axon Instruments, Inc. , Foster City, CA.
• J. Buhler, T. Ideker, and D. Haynor, "Dapple: improved techniques for finding spots on DNA microarrays," Technical Report. UWTR 2000-08-05, UV CSE, Seattle,Washington, USA.
• M. J. Buckley. (2000). The spot user's guide. CSIRO Mathematical and Information Science [Online]. Available: http://www. cmis. csiro. au/IAP/Spot/spotmanual. html.
• ImaGene, ImaGene 6. 1 User Manual. (2006. [Online] Available:-http://www. biodiscovery. com/index/papps-webfiles-action.
• S. Beucher and F. Meyer (1993), "The morphological approach to segmentation: The watershed transformation," Opt. Eng. , vol. 34, pp. 433–481.
• R. Adams and L. Bischof (Jun. 1994), "Seeded region growing," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 16, no. 6, pp. 641–647.
• D. Bozinov and J. Rahenfuhrer (2002. ), "Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering," J. Bioinform. , vol. 18, pp. 747–756.
• Y. Chen, E. R. Dougherty, and M. L. Bittne (1997), "Ratio-based decisions abd the quantitative analysis of c-DNA microarray images," J. Biomed. Opt. , vol. 2, pp. 264–374.
• S. Wu and H. Yan (2003), "Microarray Image Processing Based on Clustering and Morphological Analysis", Proc. Of First Asia-Pasific Bioinformatics Conference, Adelaide, Australia, pp. 111-118.
• Volkan Uslan and ?hsan Ömür Bucak (2010). Microarray image segmentation using clustering methods. Mathematical and Computational Applications, Vol. 15, No. 2, pp. 240-247, © Association for Scientific Research
• The Math Works, Inc. , Software, MATLABR (2010a). Natick, MA.
• MacQueen, J. B. (1967). Some Methods for classifications. In 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297. Berkeley:University of California Press
• J. C. Bezdek (1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.
• D. E. Goldberg (1989), Genetic Algorithms in Search, Optimization & Machine Learning, Boston: Addison-Wesley, Reading, ch. 1.
• L. Davis (Ed. )(1991), Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York.
• Z. Michalewicz (1992), Genetic Algorithms #Data Structures" Evolution Programs, Springer, New York.
• J. L. R. Filho, P. C. Treleaven, C. Alippi (1994), Genetic algorithm programming environments, IEEE Comput. 27, 28-43.
• U. Maulik and S. Bandyopadhyay (2000), "Genetic algorithm based clustering technique," Pattern Recog. , vol. 33, pp. 1455–1465.
• Saha, S. and Bandyopadhyay, S. , Accepted, (2007), Fuzzy Symmetry Based Real-Coded Genetic Clustering Technique for Automatic Pixel Classification in Remote Sensing Imagery. Fundamenta Informaticae.
• S. Bandyopadhyay and S. Saha (2007), "GAPS: A clustering method using a new point symmetry based distance measure," Pattern Recog. , vol. 40, pp. 3430–3451.
• F. Herrera, M. Lozano, and J. L. Verdegay (Nov 1998), "Tackling Real Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis," Artificial Intelligence Review, vol. 12, no. 4, pp. 265–319.
• O. Demirkaya, M. H. Asyali, and M. M. Shoukri (2005), "Segmentation of c-DNA microarray spots using Markov radom field modeling," Bioinformatics, vol. 21, no. 13, pp. 2994–3000.
• D. Tran and M. Wagner (2002), "Fuzzy C-means clustering-based speaker verification," in Lecture Notes in Computer Science: Advances in Soft Computing—AFSS 2002, N. R. Pal and M. Sugeno, Eds. New York: Springer-Verlag, pp. 318–324.
• D. Betal, N. Roberts, and G. H. Whitehouse (1997), "Segmentation and numerical analysis of micro calci?cations on mammograms using mathematical morphology," Br. J. Radiol. , vol. 70, no. 837, pp. 903–917.
• E. I. Athanasiadis, D. A. Cavouras, P. P. Spyridonos, D. Th. Glotsos, I. K. Kalatzis, G. C. Nikiforidis (July 2009), Complementary DNA microarray image processing based on the Fuzzy Gaussian mixture model, in: IEEE Transaction on Information Technology in Biomedicine, vol. 13, issue 4.
• E. I. Athanasiadis, D. A. Cavouras, P. P. Spyridonos, D. Th. Glotsos, I. K. Kalatzis, G. C. Nikiforidis (2011), A Wavelet based markov random field segmentation model in segmenting microarray experiments, in: Computer methods and programs in biomedicine 104,307-315.
• A. Lehmussola, et al. (2006), Evaluating the performance of microarray segmentation algorithms, Bioinformatics 22, 2910–2917.
• K. Blekas, N. Galatsanos, A. Likas, and I. E. Lagaris (Jul. 2005. ), "Mixture model analysis of DNA microarray images," IEEE Trans. Med. Imag. , vol. 24, no. 7, pp. 901–907.