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Contourlet transform-based sharpening enhancement of retinal images and vessel extraction application

  • Anand Swaminathan EMAIL logo , Shantha Selva Kumari Ramapackiam , Thivya Thiraviam and Jeeva Selvaraj
Published/Copyright: January 12, 2013

Abstract

Low-contrast retinal images have to be enhanced for good visual perception to aid in retinal vessel analysis. Classical sharpening enhancement techniques such as unsharp masking (USM) improve the contrast and bring out the information along with noise. This article uses a shift-invariant anisotropic Contourlet transform (CT) to decompose the retinal image into subbands. A new nonlinear method is applied over the subbands to modify the CT coefficients, followed by inverse CT. The proposed method is compared with a nonlinear USM (NLUSM) technique and wavelet transform-based method. The objective performance is measured in terms of enhancement measure. We observed that the proposed methodology provides better result. We demonstrate that this sharpening algorithm can be used as a preprocessing step to (i) adaptive histogram equalization and (ii) retinal vessel extraction. Pratt’s figure of merit was used to analyze the vessel extracted from the retinal images with their ground truth that were obtained from STARE and DRIVE databases.


Corresponding author: Anand Swaminathan, Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu 626005, India, Phone: +91-98421-49397, +91-4562-235410, Fax: +91-4562-235111

References

[1] Agaian SS, Panetta K, Grigoryan MA. Transform based image enhancement algorithms with performance measure. IEEE Trans Image Process 2001; 10: 367–382.10.1109/83.908502Search in Google Scholar PubMed

[2] Akram MU, Atzaz A, Aneeque SF, Khan S A. Blood vessel enhancement and segmentation using wavelet transform. IEEE International Conference on Digital Image Process 2009; 34–38.10.1109/ICDIP.2009.70Search in Google Scholar

[3] Da Cunha AL, Zhou JP, Do MN. The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 2006; 15: 3089.10.1109/TIP.2006.877507Search in Google Scholar

[4] Do MN, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 2005; 14: 2091–2106.10.1109/TIP.2005.859376Search in Google Scholar PubMed

[5] Eltoukhy MM, Faye I, Samir BB. Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Comput Med Imaging Graphics 2010; 34: 269–276.10.1016/j.compmedimag.2009.11.002Search in Google Scholar PubMed

[6] Esmaeili M, Rabbani H, Mehri A, Dehghani A. Extraction of retinal blood vessels by curvelet transform. IEEE Int Conf Image Process 2009; 3353–3356.10.1109/ICIP.2009.5413909Search in Google Scholar

[7] Feng P, Pan Y, Wei B, Jin W, Mi D. Enhancing retinal image by the contourlet transform. Pattern Recog Lett 2007; 28: 516–522.10.1016/j.patrec.2006.09.007Search in Google Scholar

[8] Ghaderi R, Hassanpour H, Shahiri M. Retinal vessel segmentation using the 2-D Morlet wavelet and neural network. IEEE Int Conf Intell Adv Syst 2007; 1251–1255.10.1109/ICIAS.2007.4658584Search in Google Scholar

[9] Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Trans Med Imaging 2000; 19: 203–210.10.1109/42.845178Search in Google Scholar PubMed

[10] Karen P, Yicong Z, Agaian SS, Jia HW. Nonlinear unsharp masking for mammogram enhancement. IEEE Trans Inform Technol Biomed 2011; 15: 918–928.10.1109/TITB.2011.2164259Search in Google Scholar PubMed

[11] Kim YH, Cho YJ. Feature and noise adaptive unsharp masking based on statistical hypotheses test. IEEE Trans Consumer Electron 2008; 54: 823–830.10.1109/TCE.2008.4560166Search in Google Scholar

[12] Ma Y, Xie J, Luo J. Image enhancement based on nonsubsampled contourlet transform. Fifth International Conference on Information Assurance and Security 2009: 31–34.10.1109/IAS.2009.44Search in Google Scholar

[13] MESSIDOR, Available at: http://messidor.crihan.fr/. Accessed on 1 July 2012.Search in Google Scholar

[14] Pratt WK. Digital image processing. New York: John Wiley & Sons Inc., 2007.Search in Google Scholar

[15] Shah VP, Younan NH, King RL. An efficient pan sharpening method via a combined adaptive PCA approach and contourlets. IEEE Trans Geosci Remote Sensing 2008; 46: 1323–1335.10.1109/TGRS.2008.916211Search in Google Scholar

[16] Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2004; 23: 501–509.10.1109/TMI.2004.825627Search in Google Scholar PubMed

[17] STARE-STructured Analysis of the Retina project. Available at: http://www.ces.clemson.edu/~ahoover/stare/. Accessed on 1 July 2012.Search in Google Scholar

[18] Truc PTH, Khan MAU, Lee YK, Lee S, Kim TS. Vessel enhancement filters using directional filter bank. Comput Vis Image Understanding 2009; 113: 101–112.10.1016/j.cviu.2008.07.009Search in Google Scholar

[19] Yang Y, Su Z, Sun L. Medical image enhancement algorithm based on wavelet transform. IET Electron Lett 2010; 46: 120–121.10.1049/el.2010.2063Search in Google Scholar

[20] Zhen H, Yewei L, Jinjiang L. Image nonlinear enhancement algorithm based on nonsubsampled contourlet transform. JDCTA 2011; 5: 43–51.10.4156/jdcta.vol5.issue7.6Search in Google Scholar

Received: 2012-8-4
Accepted: 2012-12-10
Published Online: 2013-01-12
Published in Print: 2013-02-01

©2013 by Walter de Gruyter Berlin Boston

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