Abstract
This paper presents an enhancement method for blood vessels in retinal images based on the nonsubsampled contourlet transform (NSCT). The NSCT is a shift-invariant version of the contourlet transform built upon the nonsubsampled pyramid filter banks and the nonsubsampled directional filter banks. The proposed method uses the NSCT to decompose the input retinal image into eight directions from coarser to finer scales, and then analyzes and classifies the image pixels into three categories: vessel, uncertainty, and non-vessel pixels, according to the NSCT coefficients. Then, we modify the NSCT coefficients according to the class of each pixel using a nonlinear mapping function, and reconstruct the enhanced image from the modified NSCT coefficients. The experimental results show that the proposed method can obviously increase the contrast of retinal vessels and thus outperform other enhancement methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Chen, J., & Tian, J. (2008). Retinal vessel enhancement based on directional field. In Proceedings of SPIE, 6914.
Cunha A. L., Zhou J. P., Do M. N. (2006) The nonsubsampled contourlet transform: Theory, design, and applications. IEEE Transactions on Image Processing 15(10): 3089–3101
Dippel S., Stahl M., Wiemker R., Blaffert T. (2002) Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Transactions on Medical Imaging 21(4): 343–353
Do M. N., Vetterli M. (2005) The contourlet transform: An efficient directional multiresolution image representation. IEEE Transactions on Image Processing 14(12): 2091–2106
Farnell D. J. J., Hatfield F. N., Knox P. C., Reakes M., Parry D., Spencer S., Harding S. P. (2008) Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators. Journal of the Franklin Institute 345(7): 748–765
Feng P., Pan Y., Wei B., Jin W., Mi D. (2007) Enhancing retinal image by the contourlet transform. Pattern Recognition Letters 28: 516–522
Foracchia M., Grisan E., Ruggeri A. (2005) Luminosity and contrast normalization in retinal images. Medical Image Analysis 9: 179–190
Intajag, S., Tipsuwanporn, V., & Chatthai, R. (2009). Retinal image enhancement in multi-mode histogram. In Proceeding of 2009 WRI world congress on computer science and information engineering, pp. 745–749.
Jafar, I., & Ying, H. (2007). Multilevel component-based histogram equalization for enhancing the quality of grayscale images. In Proceeding of IEEE international conference on electro/information technology, pp. 563–568
Jain A. K. (1989) Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ
Kanski J. J. (1989) Clinical Ophthalmology: A Systematic Approach. Butterworth-Heinemann, London, U.K.
Kim Y. T. (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics 43(1): 1–8
Laine A., Fan J., Yang W. (1995) Wavelets for contrast enhancement of digital mammography. IEEE Transactions on Biomedical Engineering 14: 536–550
Lee S. J., McCarty C. A., Taylor H. R., Keeffe J. E. (2001) Costs of mobile screening for diabetic retinopathy: A practical framework for rural populations. Australian Journal of Rural Health 8: 186–192
Liu, W., & Guo, S. (2007). Design and implementation of image enhancement based on pyramid architecture. In Proceeding of IEEE TENCON 2007, pp. 1–4.
Patton N., Aslam T. M., MacGillivary T., Deary I. J., Dhillon B., Eikelboom R. H., Yogesan K., Constable I. J. (2006) Retinal image analysis: Concepts, applications and potential. Progress in Retinal and Eye Research 25: 99–127
Pizer S. M., Amburn E. P., Austin J. D., Cromartie R., Geselowitz A., Greer T., Romeny B. H., Zimmerman J. B., Zuiderveld K. (1987) Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing 39(3): 355–368
Shensa M. J. (1992) The discrete wavelet transform: Wedding the à trous and Mallat algorithms. IEEE Transactions on Signal Processing 40(10): 2464–2482
Staal J. J., Abramoff M. D., Niemeijer M., Viergever M. A., van Ginneken B. (2004) Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23: 501–509
Stahl M., Aach T., Buzug T. M., Dippel S., Neitzel U. (1999) Noise-resistant weak-structure enhancement for digital radiography. Proceeding of SPIE Medical Imaging 3661: 1406–1417
Taylor H. R., Keeffe J. E. (2001) World blindness: A 21st century perspective. The British Journal of Ophthalmology 85: 261–266
Van De Ville D., Blu T., Unser M. (2005) On the multidimensional extension of the quincunx subsampling matrix. IEEE Signal Processing Letters 12(2): 112–115
Vuylsteke P., Schoeters E. (1994) Multiscale image contrast amplification (MUSICATM). Proceeding of SPIE Image Processing 2167: 551–560
Wang Y., Chen Q., Zhang B. M. (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics 45(1): 68–75
Zhang Q., Guo B. (2009) Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing 89: 1334–1346
Zong X., Laine A. F., Geiser E. A., Wilson D. C. (1996) De-noising and contrast enhancement via wavelet shrinkage and nonlinear adaptive gain. Proceeding of SPIE Wavelet Applications III 2762: 566–574
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, CC., Shih, CY., Lee, SK. et al. Enhancement of blood vessels in retinal imaging using the nonsubsampled contourlet transform. Multidim Syst Sign Process 23, 423–436 (2012). https://doi.org/10.1007/s11045-011-0167-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11045-011-0167-y