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Retrospective Non-Uniform Illumination Correction Techniques in Images of Tuberculosis

Published online by Cambridge University Press:  13 August 2014

Ebenezer Priya ,
Subramanian Srinivasan  and
Swaminathan Ramakrishnan
Ebenezer Priya*
Affiliation:
Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chrompet, Chennai-600044, India
Subramanian Srinivasan
Affiliation:
Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chrompet, Chennai-600044, India
Swaminathan Ramakrishnan
Affiliation:
Biomedical Engineering Division, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai-600036, India
*
*Corresponding author. priyabeatrice@gmail.com
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Abstract

Image pre-processing is highly significant in automated analysis of microscopy images. In this work, non-uniform illumination correction has been attempted using the surface fitting method (SFM), multiple regression method (MRM), and bidirectional empirical mode decomposition (BEMD) in digital microscopy images of tuberculosis (TB). The sputum smear positive and negative images recorded under a standard image acquisition protocol were subjected to illumination correction techniques and evaluated by error and statistical measures. Results show that SFM performs more efficiently than MRM or BEMD. The SFM produced sharp images of TB bacilli with better contrast. To further validate the results, multifractal analysis was performed that showed distinct variation before and after implementation of illumination correction by SFM. Results demonstrate that after illumination correction, there is a 26% increase in the number of bacilli, which aids in classification of the TB images into positive and negative, as TB positivity depends on the count of bacilli.

Keywords

tuberculosissputum smear imagesnon-uniform illuminationpolynomial fitmultiple regressionbidirectional empirical mode decompositionmultifractal analysis
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 20 , Issue 5 , October 2014 , pp. 1382 - 1391
Copyright
© Microscopy Society of America 2014 

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References

Abry, P., Jaffard, S. & Wendt, H. (2012). Bruegel’s drawings under the multifractal microscope. IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Kyoto, Japan, March 25–30, pp. 3909–3912.CrossRefGoogle Scholar
Babaloukas, G., Tentolouris, N., Liatis, S., Sklavounou, A. & Perrea, D. (2011). Evaluation of three methods for retrospective correction of vignetting on medical microscopy images utilizing two open source software tools. J Microsc 244(3), 320–324.CrossRefGoogle ScholarPubMed
Fernandez, E., Grana, M. & Cabello, J.R. (2004). Gradient based evolution strategy for parametric illumination correction. Electron Lett 40, 531–532.CrossRefGoogle Scholar
Finlayson, G.D., Drew, M.S. & Lu, C. (2009). Entropy minimization for shadow removal. Int J Comput Vis 85, 35–57.CrossRefGoogle Scholar
Guo, Y., Zhang, X., Zhan, H. & Song, J. (2005). A novel illumination normalization method for face recognition. In Advances in Biometric Person Authentication, Li S.Z., Sun Z. & Tan T. (Eds.), Lecture Notes in Computer Science 3781, pp. 23–30. Berlin, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
Hou, Z. (2006). A review on MR image intensity inhomogeneity correction. Int J Biomed Imaging 2006(49515), 1–11.CrossRefGoogle ScholarPubMed
Ko, J., Kim, E. & Byun, H. (2002). Illumination normalized face image for face recognition. In Structural, Syntactic, and Statistical Pattern Recognition, Caelli T., Amin A., Duin R.P.W., Kamel M. & de Ridder D. Lecture Notes in Computer Science 2396, (Eds.), pp. 654–661. Windsor, Ontario, Canada: Springer Berlin Heidelberg.CrossRefGoogle Scholar
Landini, G. (2006). How to correct background illumination in brightfield microscopy. Available at http://imagejdocu.tudor.lu/doku.php?id=howto:working:how_to_correct_background_illumination_in_brightfield_microscopy (accessed 20/2/2013).Google Scholar
Laszlo, A. (2000). Sputum Examination for Tuberculosis by Direct Microscopy in Low Income Countries, Technical Guide, 5th ed.Paris, France: International Union Against Tuberculosis and Lung Disease.Google Scholar
Lee, H. & Kim, J. (2009). Retrospective correction of nonuniform illumination on bi-level images. Opt Express 17, 23880–23893.CrossRefGoogle ScholarPubMed
Likar, B., Maintz, J.B.A., Viergever, M.A. & Pernus, F. (2000). Retrospective shading correction based on entropy minimization. J Microsc 197, 285–295.CrossRefGoogle ScholarPubMed
Lindblad, J. & Bengtsson, E. (2001). A comparison of methods for estimation of intensity nonuniformities in 2D and 3D microscope images of fluorescence stained cells. Proceedings of the 12th Scandinavian Conference on Image Analysis, Stavanger University College, Stavanger, Norway, June 11–14, pp. 264–271.Google Scholar
Liu, X.Y., Wang, W.H. & Sun, Y. (2006). Autofocusing for automated microscopic evaluation of blood smear and pap smear. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, IEEE Engineering in Medicine and Biology Society, New York, NY, USA, August 30–September 3, pp. 4718–4721.CrossRefGoogle Scholar
Lopes, R. & Betrouni, N. (2009). Fractal and multifractal analysis: A review. Med Image Anal 13, 634–649.CrossRefGoogle ScholarPubMed
Lumb, R. & Bastian, I. (2005). Laboratory Diagnosis of Tuberculosis by Sputum Microscopy, The Handbook. Pacific island countries: Institute of Medical and Veterinary Science.Google Scholar
Nunes, J.C., Bouaoune, E., Delechelle, E., Niang, O. & Bunel, P.H. (2003). Image analysis by bidimensional empirical mode decomposition. Image Vision Comput 21, 1019–1026.CrossRefGoogle Scholar
Ogier, A., Dorval, T. & Genovesio, A. (2007). Biased image correction based on empirical mode decomposition. IEEE International Conference on Image Processing, IEEE, San Antonio, TX, September 16–October 19, pp. 533–536.CrossRefGoogle Scholar
Paruchuri, J.K., Sathiyamoorthy, E.P., Cheung, S.S. & Chen, C.H. (2011). Spatially adaptive illumination modeling for background subtraction. Proceedings of International Conference on Computer Vision Workshops, Technical University of Catalonia, Barcelona, Spain, November 6–13, pp. 1745–1752.CrossRefGoogle Scholar
Qin, X., Liu, S., Zhengqiang, W. & Han, J. (2008). Medical image enhancement method based on 2D empirical mode decomposition. The 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China, pp. 2533–2536.CrossRefGoogle Scholar
Reyes-Aldasoro, C.C. (2009). Retrospective shading correction algorithm based on signal envelope estimation. Electron Lett 45(9), 454–456.CrossRefGoogle Scholar
Ryu, S. & Sohn, K. (2011). No-reference sharpness metric based on inherent sharpness. Electron Lett 47, 1178–1180.CrossRefGoogle Scholar
Steingart, K.R., Henry, M., Ng, V., Hopewell, P.C., Ramsay, A., Cunningham, J., Urbanczik, R., Perkins, M., Aziz, M.A. & Pai, M. (2006). Fluorescence versus conventional sputum smear microscopy for tuberculosis: A systematic review. Lancet Infect Dis 6, 570–581.CrossRefGoogle ScholarPubMed
Tasdizen, T., Whitaker, R., Marc, R. & Jones, B. (2005). Automatic correction of non-uniform illumination in transmission electron microscopy images, SCI Institute Technical Report, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT.CrossRefGoogle Scholar
Vasiljevic, J., Reljin, B., Sopta, J., Mijucic, V., Tulic, G. & Reljin, I. (2012). Application of multifractal analysis on microscopic images in the classification of metastatic bone disease. Biomed Microdevices 14, 541–548.CrossRefGoogle ScholarPubMed
Vlachos, M. & Dermatas, E. (2012). Blind retrospective shading correction using a multi-objective minimization criterion. Comput Med Imaging Graph 36, 501–513.CrossRefGoogle ScholarPubMed
Vlachos, M.D. & Dermatas, E.S. (2013). Non-uniform illumination correction in infrared images based on a modified fuzzy c-means algorithm. J Biomed Graph Comput 3(1), 6–19.Google Scholar
Wahlby, C., Lindblad, J., Vondrus, M., Bengtsson, E. & Bjorkesten, L. (2002). Algorithms for cytoplasm segmentation of fluorescence labelled cells. Anal Cell Pathol 24, 101–111.CrossRefGoogle ScholarPubMed
Wee, C.-Y. & Paramesran, R. (2008). Image sharpness measure using Eigenvalues. 9th International Conference on Signal Processing, Beijing Jiaton University, Beijing, China, October 26–29, pp. 840–843.Google Scholar
Wibawanto, H., Susanto, A., Widodo, T.S. & Tjokronegoro, S.M. (2010). Discriminating cystic and non cystic mass using GLCM and GLRLM-based texture features. Int J Electron Eng Res 2, 569–580.Google Scholar
Wu, Q., Merchant, F.A. & Castleman, K.R. (2008). Microscope Image Processin g. Philadelphia, PA, USA: Academic Press, Elsevier.Google Scholar
Xu, Y., Ji, H. & Fermuller, C. (2009). Viewpoint invariant texture description using fractal analysis. Int J Comput Vis 83, 85–100.CrossRefGoogle Scholar
Yuehui, S. & Minghui, D. (2008). Robust face recognition for illumination removal using DT-CWT and EMD. 11th IEEE Singapore International Conference on Communication Systems, IEEE, Guangzhou, China, November 19–21, pp. 357–361.Google Scholar
Zheng, Y., Grossman, M., Awate, S.P. & Gee, J.C. (2009). Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images. Med Image Comput Comput Assist Interv 12(2), 852–859.Google ScholarPubMed