Patch-Based Denoising with K-Nearest Neighbor and SVD for Microarray Images

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 763)

Abstract

Irrespective of certain major advancement in filtering process in medical images, the denoising operation in microarray images are still considered to be unsolved and offers a large scope of research. Existing denoising principles are less investigated on such complex and massive dimensional microarray image that leads to the development of the proposed system. We present a method of performing simple denoising operation considering the presence of Gaussian noise in microarray image. From the target image denoising method, an improved version of patch-based denoising approach has been developed considering various forms of distance-based matching methods. The study outcome of the proposed system has been found to offer better peak signal-to-noise ratio and structural similarity index in contrast to existing filtering techniques.

Keywords

Filtering Denoising Microarray image Patch-based Euclidean distance KNN Patch matching 

References

  1. 1.
    Kumar, A., Shaik, F., Abdul Rahim, B., Sravan Kumar, D.: Signal and Image Processing in Medical Applications. Springer (2016)Google Scholar
  2. 2.
    Srimani Shanthi Mahesh, P.K.: An effective automated method for detection of grids in DNA microarray. In: Springer-ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India, vol. II, pp. 445–453 (2014)Google Scholar
  3. 3.
    Yakovlev, A.Y., Klebanov, L., Gaile, D.: Statistical Methods for Microarray Data Analysis: Methods and Protocols. Springer (2013)Google Scholar
  4. 4.
    Rueda, L.: Microarray Image and Data Analysis: Theory and Practice. CRC Press (2014)Google Scholar
  5. 5.
    Fraser, K., Wang, Z., Liu, X.: Microarray Image Analysis: An Algorithmic Approach. CRC Press (2010)CrossRefGoogle Scholar
  6. 6.
    Scherer, A.: Batch Effects and Noise in Microarray Experiments: Sources and Solutions. Wiley (2009)Google Scholar
  7. 7.
    Gohlmann, H., Talloen, W.: Gene Expression Studies Using Affymetrix Microarrays. CRC Press (2009)Google Scholar
  8. 8.
    Do, K.-A., Müller, P., Vannucci, M.: Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press (2006)Google Scholar
  9. 9.
    Balding, D.J., Bishop, M., Cannings, C.: Handbook of Statistical Genetics. Wiley (2008)Google Scholar
  10. 10.
    Kuzhali, E., Suresh, D.S.: A comprehensive study of enhancement and segmentation techniques on microarray images. i-manager’s J. Pattern Recognit. 2(2) (2015)Google Scholar
  11. 11.
    Pad, P., Alishahi, K., Unser, M.: Optimized wavelet denoising for self-similar α-stable processes. IEEE Trans. Inf. Theo. 63(9), 5529–5543 (2017)MathSciNetMATHGoogle Scholar
  12. 12.
    Boubchir, L., Boashash, B.: Wavelet denoising based on the MAP estimation using the BKF prior with application to images and EEG signals. IEEE Trans. Sig. Process. 61(8), 1880–1894 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lahmiri, S.: Denoising techniques in adaptive multi-resolution domains with applications to biomedical images. Healthc. Technol. Lett. 4(1), 25–29 (2017)Google Scholar
  14. 14.
    Zeng, X., Bian, W., Liu, W., Shen, J., Tao, D.: Dictionary pair learning on grassmann manifolds for image denoising. IEEE Trans. Image Process. 24(11), 4556–4569 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wang, G., Wang, Z., Liu, J.: A new image denoising method based on adaptive multiscale morphological edge detection. Hindawi Math. Prob. Eng. (2017)Google Scholar
  16. 16.
    Guo, Q., Zhang, C., Zhang, Y., Liu, H.: An efficient SVD-based method for image denoising. IEEE Trans. Circuits Syst. Video Technol. 26(5), 868–880 (2016)CrossRefGoogle Scholar
  17. 17.
    Golshan, H.M., Hasanzadeh, R.P.R.: An optimized LMMSE based method for 3D MRI denoising. IEEE/ACM Trans. Comput. Biol. Bioinform 12(4), 861–870 (2015)CrossRefGoogle Scholar
  18. 18.
    Buades, A., Lisani, J.L., Miladinović, M.: Patch-based video denoising with optical flow estimation. IEEE Trans. Image Process. 25(6), 2573–2586 (2016)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Chen, X., Kang, S.B., Yang, J., Yu, J.: Fast edge-aware denoising by approximated patch geodesic paths. IEEE Trans. Circuits Syst. Video Technol. 25(6), 897–909 (2015)CrossRefGoogle Scholar
  20. 20.
    Feng, J., Song, L., Huo, X., Yang, X., Zhang, W.: An optimized pixel-wise weighting approach for patch-based image denoising. IEEE Sig. Process. Lett. 22(1), 115–119 (2015)CrossRefGoogle Scholar
  21. 21.
    Bhujle, H., Chaudhuri, S.: Novel speed-up strategies for non-local means denoising with patch and edge patch based dictionaries. IEEE Trans. Image Process. 23(1), 356–365 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Cao, M., Li, S., Wang, R., Li, N.: Interferometric phase denoising by median patch-based locally optimal wiener filter. IEEE Geosci. Remote Sens. Lett. 12(8), 1730–1734 (2015)CrossRefGoogle Scholar
  23. 23.
    Chatterjee, P., Milanfar, P.: Patch-based near-optimal image denoising. IEEE Trans. Image Process. 21(4), 1635–1649 (2012)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Zhong, H., Han, P.P., Zhang, X.H., Yu, Y.Q.: Hybrid patch similarity for image denoising. Electron. Lett. 48(4), 212–213 (2012)CrossRefGoogle Scholar
  25. 25.
    Boulanger, J., Kervrann, C., Bouthemy, P., Elbau, P., Sibarita, J.B., Salamero, J.: Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Trans. Med. Imaging 29(2), 442–454 (2010)CrossRefGoogle Scholar
  26. 26.
    Deledalle, C.A., Denis, L., Tupin, F.: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. Image Process. 18(12), 2661–2672 (2009)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Khalilabad, N.D., Hassanpour, H.: Employing image processing techniques for cancer detection using microarray images. Comput. Biol. Med. 81, 139–147 (2017)CrossRefGoogle Scholar
  28. 28.
    Wang, X.H., Istepanian, R.S.H., Song, Y.H.: Microarray image enhancement by denoising using stationary wavelet transform. IEEE Trans. Nanobiosci. 2(4), 184–189 (2003)CrossRefGoogle Scholar
  29. 29.
    Stefanou, H., Margaritis, T., Kafetzopoulos, D., Marias, K., Tsakalides, P.: Microarray image denoising using a two-stage multiresolution technique. In: 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), Fremont, CA, pp. 383–389 (2007)Google Scholar
  30. 30.
    Mastriani, M., Giraldez, A.E.: Microarrays denoising via smoothing of coefficients in wavelet domain. World Acad. Sci. Eng. Technol. Int. J. Electron. Commun. Eng. 1(2) (2007)Google Scholar
  31. 31.
    Howlader, T., Chaubey, Y.P.: Noise reduction of cDNA microarray images using complex wavelets. IEEE Trans. Image Process. 19(8), 1953–1967 (2010)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Vishakha, P.S, Supriya, S.T.: Study and analysis of microarray denoising using systholic boolean orthonormalizer network in wavelet domain. In: Advancement in Electronics and Telecommunication Engineering (2012)Google Scholar
  33. 33.
    Fouad, I.A., Mabrouk, M.S., Sharawy, A.A.: A new method to grid noisy cDNA microarray images utilizing denoising techniques. Int. J. Comput. Appl. 63(9), February 2013Google Scholar
  34. 34.
    Srinivasan, L., Rakvongthai, Y., Oraintara, S.: Microarray image denoising using complex gaussian scale mixtures of complex wavelets. IEEE J. Biomed. Health Inf. 18(4), 1423–1430 (2014)CrossRefGoogle Scholar
  35. 35.
    Nykter, M., Aho, T., Ahdesmäki, M., Ruusuvuori, P., Lehmussola, A., Yli-Harja, O.: Simulation of microarray data with realistic characteristics. BMC Bioinf. 7(1), 349 (2006)CrossRefGoogle Scholar
  36. 36.
    Stanford Microarray Database (SMD), http://smd.stanford.edu/
  37. 37.
    Luo, E.: Statistical and adaptive patch-based image denoising. A Doctorial Dissertation of University of California, San Diego (2016)Google Scholar
  38. 38.
    Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. SIAM Multiscale Model Simul. 4(2), 490–530 (2005)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: BM3D image denoising with shape-adaptive principal component analysis. In: Signal Process with Adaptive Sparse Structured Representations (SPARS 2009), pp. 1–6, April 2009Google Scholar
  40. 40.
    Zhang, L., Dong, W., Zhang, D., Shi, G.: Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recogn. 43, 1531–1549 (2010)CrossRefGoogle Scholar
  41. 41.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.VTU Research CentreCITTumkurIndia
  2. 2.Channabasaveshwara Institute of TechnologyTumkurIndia

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