Application of the adaptive center-weighted vector median framework for the enhancement of cDNA microarray images

Rastislav Lukac; Bogdan Smołka

International Journal of Applied Mathematics and Computer Science (2003)

  • Volume: 13, Issue: 3, page 369-383
  • ISSN: 1641-876X

Abstract

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In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing the sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of the so called gene chips. We demonstrate that the new technique is capable of reducing the impulsive noise present in microarray images and that it facilitates efficient spot location and the estimation of the gene expression levels due to the smoothing effect and preservation of the spot edges. This paper contains a comparison of the new technique of impulsive noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.

How to cite

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Lukac, Rastislav, and Smołka, Bogdan. "Application of the adaptive center-weighted vector median framework for the enhancement of cDNA microarray images." International Journal of Applied Mathematics and Computer Science 13.3 (2003): 369-383. <http://eudml.org/doc/207651>.

@article{Lukac2003,
abstract = {In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing the sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of the so called gene chips. We demonstrate that the new technique is capable of reducing the impulsive noise present in microarray images and that it facilitates efficient spot location and the estimation of the gene expression levels due to the smoothing effect and preservation of the spot edges. This paper contains a comparison of the new technique of impulsive noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.},
author = {Lukac, Rastislav, Smołka, Bogdan},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {order-statistic theory; impulsive noise; DNA microarray images; multichannel image processing; vector filters; noise reduction in color images},
language = {eng},
number = {3},
pages = {369-383},
title = {Application of the adaptive center-weighted vector median framework for the enhancement of cDNA microarray images},
url = {http://eudml.org/doc/207651},
volume = {13},
year = {2003},
}

TY - JOUR
AU - Lukac, Rastislav
AU - Smołka, Bogdan
TI - Application of the adaptive center-weighted vector median framework for the enhancement of cDNA microarray images
JO - International Journal of Applied Mathematics and Computer Science
PY - 2003
VL - 13
IS - 3
SP - 369
EP - 383
AB - In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing the sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of the so called gene chips. We demonstrate that the new technique is capable of reducing the impulsive noise present in microarray images and that it facilitates efficient spot location and the estimation of the gene expression levels due to the smoothing effect and preservation of the spot edges. This paper contains a comparison of the new technique of impulsive noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.
LA - eng
KW - order-statistic theory; impulsive noise; DNA microarray images; multichannel image processing; vector filters; noise reduction in color images
UR - http://eudml.org/doc/207651
ER -

References

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  1. Ajay N., Tokuyasu T., Snijders A., Segraves R., Albertson D. and Pinkel D.(2002): Fully automatic quantification of microarray image data. - Genome Res., Vol. 12, No. 2, pp. 325-332. 
  2. Alparone L., Barni M., Bartolini F. and Caldelli R. (1999): Regularization of optic flow estimates by means of weighted vector median filtering. - IEEE Trans. Image Process., Vol. 8, No. 10, pp. 1462-1467. 
  3. Astola J., Haavisto P. and Neuvo Y. (1990): Vector median filters. - Proc. IEEE, Vol. 78, No. 4, pp. 678-689. 
  4. Astola J. and Kuosmanen P. (1997): Fundamentals of Nonlinear Digital Filtering. - Boca Raton: CRC Press. Zbl0939.68911
  5. Bardos A.J. and Sangwine S.J.(1997): Selective vector median filtering of colour images. - Proc. 6th Int. Conf. Image Processing and Its Applications, Dublin, Ireland, Vol. 2, pp. 708-711. 
  6. Boncelet C. (2000): Image noise models, In: Handbook of Image and Video Processing (Bovik A., Ed.). - New York: Academic Press. 
  7. Bozinov D. and Rahnenfuhrer J.(2002): Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. - Bioinformat., Vol. 18, No. 5, pp. 747-756. 
  8. Chen T. and Wu H.R.(2001): Adaptive impulse detection using center-weighted median filters. - IEEE Signal Process. Lett., Vol. 8, No. 1, pp. 1-3. 
  9. Chen T., Ma K.K. and Chen L.H.(1999): Tri-state median filter for image denoising. - IEEE Trans. Image Process., Vol. 8, No. 12, pp. 1834-1838. 
  10. Chen Y., Dougherty E.R. and Bittner M.L. (1997): Ratio-based decisions and the quantitative analysis of cDNA microarray images. - J. Biomed. Optics, Vol. 2, No. 4, pp. 364-374. 
  11. Conway T., Kraus B., Tucker D.L., Smalley D.J., Dorman A.F. and McKibben L.(2002): DNA array analysis in a Microsoft Windows environment. - Biotechniques, Vol. 32, No. 1, pp. 110-116. 
  12. Dopazo J. (2002): Microarray data processing and analysis, In: Microarray Data Analysis II (Lin S.M. and Johnson K.F., Eds.). - Boston: Kluwer, pp. 43-63. 
  13. Eisen M.B. and Brown P.O.(1999): DNA arrays for analysis of gene expression.. - Methods in Enzymology, Vol. 303, pp. 179-205. 
  14. Filkov V., Skiena S. and Zhi J.(2002): Analysis techniques for microarray time-series data. - J. Comput. Biol., Vol. 9, No. 2, pp. 317-330. 
  15. Gabbouj M. and Cheickh F.A. (1996): Vector Median-Vector Directional Hybrid Filter for Color Image Restoration. - Proc. 8th Europ. Signal Processing Conference, EUSIPCO-96, Trieste, Italypp. 879-881. 
  16. Hsiao L., Jensen R., Yoshida T., Clark K., Blumenstock J. and Gullans S. (2002): Correcting for signal saturation errors in the analysis of microarray data. - Biotechniques, Vol. 32, No. 2, pp. 330-336. 
  17. Karakos D.G. and Trahanias P.E. (1997): Generalized multichannel image-filtering structure. - IEEE Trans. Image Process., Vol. 6, No. 7, pp. 1038-1045. 
  18. Leung Y.(2002): Microarray data analysis for dummies ... and experts too? - Trends Biochem. Sci., Vol. 27, No. 8, pp. 433-434. 
  19. Lukac R. (2002): Color image filtering by vector directional order-statistics.- Pattern Recognition and Image Analysis, Vol. 12, No. 3, pp. 279-285. 
  20. Lukac R. (2003): Adaptive vector median filtering. - Pattern Recognition Letters. Vol. 24, No. 12, pp. 1889-1899. 
  21. Lukac R. and Marchevsky S. (2001a): LUM smoother with smooth control for noisy image sequences. - EURASIP J. Appl. Signal Process., Vol. 2001, No. 2, pp. 110-120. Zbl0991.68603
  22. Lukac R. and Marchevsky S. (2001b): Adaptive vector LUM smoother. - Proc. IEEE Int. Conf. sImage Processing, ICIP'2001, Thessaloniki, Greece, Vol. 1, pp. 878-881. Zbl0991.68603
  23. Lukac R., Smołka B. and Plataniotis K.N. (2002): Color sigma filter. - Proc. Int. Workshop Systems, Signals and Image Processing, IWSSIP'02, Manchester, U.K., pp. 559-565. 
  24. Lukac R., Plataniotis K.N., Smołka B. and Ventesanopulos A.N. (2003a): Generalized sigmoidal optimization of selection weighted vector filters. - Proc. IEEE-EURASIP Workshop Nonlinear Signal and Image Processing, NSIP'03, Grado, Italy, (accepted). 
  25. Lukac R., Plataniotis K.N., Smołka B. and Ventesanopulos A.N. (2003b): Weighted vector median optimization. - Proc. 4th EURASIP Conf. Video ImageProcessing and Multimedia Communications, EC-VIP-MC'03, Zagreb, Croatia, (accepted). 
  26. Lucat L., Siohan P. and Barba D.(2002): Adaptive and global optimization methods for weighted vector median filters. - Signal Process. Image Comm., Vol. 17, No. 7, pp. 509-524. 
  27. Mitra S.J. and Sicuranza G.L. (2001): Nonlinear Image Processing. - New York: Academic Press. 
  28. Peltonen S., Gabbouj M. and Astola J. (2001): Nonlinear filter design: Methodologies and challenges. - Proc. 2nd IEEE Region 8-EURASIP Symp. Image and Signal Processing and Analysis, ISPA'01, Pula, Croatia, pp. 102-107. 
  29. Pitas I. and Tsakalides P. (1991): Multivariate ordering in color image filtering. - IEEE Trans. Circ. Syst. Video Technol., Vol. 1, No. 3, pp. 247-259. 
  30. Pitas I. and Venetsanopoulos A.N. (1990): Nonlinear Digital Filters, Principles and Applications. - Boston: Kluwer. Zbl0719.93080
  31. Pitas I. and Venetsanopoulos A.N. (1992): Order statistics in digital image processing. - Proc. IEEE, Vol. 80, No. 12, pp. 1892-1919. 
  32. Plataniotis K.N. and Venetsanopoulos A.N. (2000): Color Image Processing and Applications. - Berlin: Springer. 
  33. Plataniotis K.N., Androutsos D. and Venetsanopoulos A.N. (1998): Color image processing using adaptive vector directional filters. - IEEE Trans. Circ. Syst. II, Vol. 45, pp. 1414-1419. 
  34. Schena M., Shalon D., Davis R.W. and Brown P.O. (1995): Quantitative monitoring of gene expression patterns with a complimentary DNA microarray. - Science, Vol. 270, pp. 467-470. 
  35. Smołka B., Chydziński A., Wojciechowski K., Plataniotis K.N. and Venetsanopoulos A.N. (2001): On the reduction of impulsive noise in multichannel image processing. - Optical Eng., Vol. 40, No. 6, pp. 902-908. 
  36. Smołka B., Lukac R. and Plataniotis K.N. (2002): New algorithm for noise attenuation in color images based on the central weighted vector median filter. - Proc. 9th Int. Workshop Systems Signals and Image Processing, IWSSIP'02, Manchester, U.K., pp. 544-548. 
  37. Szczepański M., Smołka B., Plataniotis K.N. and Venetsanopoulos A.N. (2002): Robust Filter for Noise Reduction in Color Images. - Proc. 1st Europ. Conf. Color in Graphics, Image and Vision, CGIV'02, Poitiers, France, pp. 517-522. 
  38. Tang K., Astola J. and Neuvo Y. (1995): Nonlinear multivariate image filtering techniques. - IEEE Trans. Image Process., Vol. 4, No. 6, pp. 788-798. 
  39. Trahanias P.E. and Venetsanopoulos A.N. (1993): Vector directional filters-a new class of multichannel image processing filters. - IEEE Trans. Image Process., Vol. 2, No. 4, pp. 528-534. 
  40. Trahanias P.E., Karakos D. and Venetsanopoulos A.N. (1996): Directional processing of color images: Theory and experimental results. - IEEE Trans. Image Process., Vol. 5, No. 6, pp. 868-881. 
  41. Viero T., Oistamo K. and Neuvo Y. (1994): Three-dimensional median related filters for color image sequence filtering. - IEEE Trans. Circ. Syst. Video Technol., Vol. 4, No. 2, pp. 129-142. 
  42. Yang Y., Buckley M., Dudoit S. and Speed T. (2002): Comparison of methods for image analysis on cDNA microarray data. - J. Comput. Graphic Stat., Vol. 11, No. 1, pp. 108-136. 
  43. Yin L., Yang R., Gabbouj M. and Neuvo Y. (1996): Weighted median filters: A tutorial. -IEEE Trans. Circ. Syst. II, Vol. 43, No. 3, pp. 157-192. 

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