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Possibilistic Reformed Fuzzy Local Information Clustering Technique for Noisy Microarray Image Spots Segmentation


Affiliations
1 Department of Electronics and Communication Engineering, College of Engineering Munnar, Munnar-685 612, India
2 Division of Electronics, School of Engineering, Cochin University of Science and Technology, Cochin-682 022, India
 

The cDNA microarray image provides useful information about thousands of gene expressions simultaneously. This information is used by bioinformatics researchers for diagnosis of different diseases and drug designs. Microarray image spot segmentation using an improved fuzzy clustering algorithm is proposed in this article. The proposed Possibilistic Reformed Fuzzy Local Information C Means (PRFLICM) algorithm is a variant of Possibilistic Fuzzy Local Information C Means (PFLICM) algorithm. The parameters used for testing the proposed algorithm include segmentation matching factor (SMF), probability of error (pe), discrepancy distance (D), normalized mean square error and sum of square distance (SSD). The performance of the algorithm is validated with a set of simulated cDNA microarray images with known gene expression values. From the results of SMF, the proposed PRFLICM shows an improvement of 0.4% and 0.1% for high noise and low noise microarray images respectively when compared to PFLICM algorithm. The proposed algorithm is applied to yeast microarray database (YMD) and is used to find the yeast cell life cycle generated genes. The results show that the proposed algorithm has identified 101 cell life cycle regulated genes out of 104 such genes published in the YMD database.

Keywords

Fuzzy Clustering, Gene Expression, Image Processing, Microarray.
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  • Yang, Y. H., Buckley, M. J., Dudoit, S. and Speed, T. P., Comparison of methods for image analysis on cDNA microarray data. J. Comput. Graph. Stat., 2002, 11(1), 108–136.

  • Lehmussola, A., Ruusuvuori, P. and Yli-Harja, O., Evaluating the performance of microarray segmentation algorithms. Bioinformatics, 2006, 22(23), 2910–2917.

  • Schena, M., Shalon, D., Davis, R. W. and Brown, P. O., Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science-New York Then Washington, 1995, pp. 467–467.

  • Zacharia, E. and Maroulis, D., 3-D spot modeling for automatic segmentation of cDNA microarray images. IEEE Trans. Nanobiosci., 2010, 9(3), 181–192.

  • Athanasiadis, E., Cavouras, D., Spyridonos, P., Glotsos, D., Kalatzis, I. and Nikoforidis, G., Segmentation of microarray images using gradient vector flow active contours boosted by Gaussian mixture models. In Second International Conference on Experiments/ Process/System Modeling/Simulation/Optimization (2nd IC-EpsMsO), Athens, Greece, 2007.

  • Athanasiadis, E., Cavouras, D., Kostopoulos, S., Glotsos, D., Kalatzis, I. and Nikiforidis, G., A wavelet-based Markov random field segmentation model in segmenting microarray experiments. Comput. Methods Programs Biomed., 2011, 104(3), 307–315.

  • Uslan, V. and Bucak, I. Ö., Microarray image segmentation using clustering methods. Math. Comput. Appl., 2010, 15(2), 240– 247.

  • Biju, V. G. and Mythili, P., A genetic algorithm based fuzzy C mean clustering model for segmenting microarray images. Int. J. Comput. Appl., 2012, 52(11), 42–48.

  • Pal, N. R., Pal, K., Keller, J. M. and Bezdek, J. C., A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst., 2005, 13(4), 517–530.

  • Krinidis, S. and Chatzis, V., A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process., 2010, 19(5), 1328–1337.

  • Biju, V. G. and Mythili, P., Fuzzy clustering algorithms for cDNA microarray image spots segmentation. Procedia Comput. Sci., 2015, 46, 417–424.

  • Biju, V. G. and Mythili, P., An improved fuzzy clustering algorithm for microarray spots segmentation. ICTACT J. Image Video Process., 2015, 6(2), 1107–1114.

  • Biju, V. G. and Mythili, P., Microarray image gridding using grid line refinement technique. ICTACT J. Image Video Process., 2015, 5(4), 1010–1016.

  • Gong, M., Zhou, Z. and Ma, J., Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process., 2012, 21(4), 2141–2151.

  • Spellman, P. T. et al., Comprehensive identification of cell cycle– regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell, 1998, 9(12), 3273–3297.

  • Tran, D. and Wagner, M., Noise clustering-based speaker verification. Lecture Notes in Computer Science, 2002, pp. 325–331.

  • Betal, D., Roberts, N. and Whitehouse, G. H., Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology. Br. J. Radiol., 1997, 70(837), 903–917.

  • Athanasiadis, E. I., Cavouras, D. A., Spyridonos, P. P., Glotsos, D. T., Kalatzis, I. K. and Nikiforidis, G. C., Complementary DNA microarray image processing based on the fuzzy Gaussian mixture model. IEEE Trans. Inform. Technol. Biomed., 2009, 13(4), 419– 425.

  • Wang, Y. P., Gunampally, M., Chen, J., Bittel, D., Butler, M. G. and Cai, W. W., A comparison of fuzzy clustering approaches for quantification of microarray gene expression. J. Signal Process. Syst., 2008, 50(3), 305–320.


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  • Possibilistic Reformed Fuzzy Local Information Clustering Technique for Noisy Microarray Image Spots Segmentation

Abstract Views: 340  |  PDF Views: 110

Authors

V. G. Biju
Department of Electronics and Communication Engineering, College of Engineering Munnar, Munnar-685 612, India
P. Mythili
Division of Electronics, School of Engineering, Cochin University of Science and Technology, Cochin-682 022, India

Abstract


The cDNA microarray image provides useful information about thousands of gene expressions simultaneously. This information is used by bioinformatics researchers for diagnosis of different diseases and drug designs. Microarray image spot segmentation using an improved fuzzy clustering algorithm is proposed in this article. The proposed Possibilistic Reformed Fuzzy Local Information C Means (PRFLICM) algorithm is a variant of Possibilistic Fuzzy Local Information C Means (PFLICM) algorithm. The parameters used for testing the proposed algorithm include segmentation matching factor (SMF), probability of error (pe), discrepancy distance (D), normalized mean square error and sum of square distance (SSD). The performance of the algorithm is validated with a set of simulated cDNA microarray images with known gene expression values. From the results of SMF, the proposed PRFLICM shows an improvement of 0.4% and 0.1% for high noise and low noise microarray images respectively when compared to PFLICM algorithm. The proposed algorithm is applied to yeast microarray database (YMD) and is used to find the yeast cell life cycle generated genes. The results show that the proposed algorithm has identified 101 cell life cycle regulated genes out of 104 such genes published in the YMD database.

Keywords


Fuzzy Clustering, Gene Expression, Image Processing, Microarray.

References





DOI: https://doi.org/10.18520/cs%2Fv113%2Fi06%2F1072-1080