Abstract
The diagnostic features of retinal images undergo changes in the course of processing such as for storage, retrieval and transmission. The conventional mean square error and peak signal to noise ratio have limitations in quantifying these local distortions. In this work, a novel wavelet weighted distortion measure (WWDM) is proposed for accurate quantification of diagnostic information loss. The wavelet analysis of retinal image shows that the significant information of a retinal feature is captured by a few subbands. The new approach is based on assigning a weight to each of the subbands depending on its diagnostic significance. The proposed distortion measure is defined as the sum of wavelet weighted root of the normalized mean square error of subbands expressed in percentage. The experimental results show that WWDM performs better in capturing the distortion in retinal features, whereas for distortion in clinically nonsignificant regions, it gives a low value. The qualitative evaluation using Pearson linear correlation coefficient and Spearman rank order correlation coefficient is performed for different artifacts. The investigation shows better correlation values between WWDM and the subjective scores.
Similar content being viewed by others
References
Eikelboom R.H., Yogesan K., Barry C.J., Constable I.J., Kearney M.T., Jitskaia L., House P.H.: Methods and limits of digital image compression of retinal images for telemedicine. Investig. Ophthalmol. Vis. Sci. 41(7), 1916–1924 (2000)
Pizurica A., Philips W.: A versatile wavelet domain noise filtration technique for medical imaging. In: IEEE Trans. Med. Imag. 22(3), 323–331 (2003)
Hwang W., Chine C., Li K.: Scalable medical data compression and transmission using wavelet transform for telemedicine applications. In: IEEE Trans. Inf. Technol. Biomed. 7(1), 54–63 (2003)
Cosman P., Gray R.M., Olshen R.A.: Evaluating the quality of compressed medical images: SNR, subjective rating and diagnostic accuracy. In: Proc. IEEE 82(6), 919–932 (1994)
Wong S., Zaremba L., Gooden D., Huang H.K.: Radiological image compression: a review. Proc. IEEE 83(2), 194–219 (1995)
Cree M.J., Jelinek H.F.: The effect of JPEG compression on automated detection of microaneurysms in retinal images. Proc. SPIE-IS&T Electron. Imaging 6813, 68130M–68130M-10 (2008)
Eskicioglu, A.M.: Quality measurement for monochrome compressed images in the past 25 years. In: Proceedings of IEEE International Conference on Acoustics, Speech, Signal Processing, Istanbul, Turkey, vol. 4, pp. 1907–1910 (2000)
Lai Y.K., Kuo C.C.J.: A Harr wavelet approach to compressed image quality measurements. J. Vis. Commun. Image Represent. 11, 17–40 (2000)
Dumic E., Grgic S., Grgic M.: New image-quality measure based on wavelets. J. Electron. Imaging 19(1), 011–018 (2010)
Gao Z., Zheng Y.F.: Quality constrained compression using DWT-based image quality metric. In: IEEE Trans. Circuits Syst. Video Technol. 18(7), 910–922 (2008)
Lee, S., Wang, Y.: Automatic retinal image quality assessment and enhancement. In: Proceedings of SPIE Image Processing, SPIE Conference on Image Processing, pp. 1581–1590 (1999)
Lalonde, M., Gagnon, L., Boucher, M.C.: Automatic visual quality assessment in optical fundus images. In: Proceedings of the Vision Interface, pp. 259–264 (2001)
Wang Z., Bovik A.C., Sheikh H., Simoncelli E.P.: Image quality assessment: from error visibility to structural similarity. In: IEEE Trans. Image Process. 13(4), 600–612 (2004)
Mallat S.: A theory for multiresolution signal decomposition: the wavelet representation. In: IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Unser M., Aldroubi A.: A review of wavelets in biomedical applications. Proc. In: IEEE 84, 626–638 (1996)
Sinthanayothin C., Boyce J., Cook H., Williamson T.: Automated localization of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. 83(8), 902–910 (1999)
Mahfouz A.E., Fahmy A.S.: Fast localization of the Optic disc using projection of image features. In: IEEE Trans. Image Process. 19(12), 3285–3289 (2010)
Nirmala S.R., Dandapat S., Bora P.K.: Wavelet weighted blood vessel distortion measure for retinal images. Biomed. Signal Process. Control 5(4), 282–291 (2010)
Rosso O., Blanco S., Yordanova J., Kolev V., Figliola A., Sahurmann M., Basar E.: Wavelet entropy: a new tool for analysis of short brain electrical signal. Neurosci. Methods 105, 65–75 (2001)
Chen T.J., Chuang K.S., Wu J., Chen S.C., Hwang I.M., Jan M.L.: Quality degradation in lossy wavelet image compression. J. Digit. Imaging 16(2), 210–215 (2003)
VQEG.: final report from the video quality experts group on the validation of objective models of video quality assessment (online). Available: http://www.vqeg.org (2003)
Wallace G.K.: The JPEG still picture compression standard. In: IEEE Trans. Consum. Electron. 38(1), 18–34 (1992)
Said A., Pearlman W.A.: A new fast and efficient image codec based on set partitioning in hierarchical trees. In: IEEE Trans. Circuits Syst. Video Technol. 6(3), 243–250 (1996)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nirmala, S.R., Dandapat, S. & Bora, P.K. Wavelet weighted distortion measure for retinal images. SIViP 7, 1005–1014 (2013). https://doi.org/10.1007/s11760-012-0290-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-012-0290-8