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Identification of noise in the fundus images | IEEE Conference Publication | IEEE Xplore

Identification of noise in the fundus images


Abstract:

Analysis of the tiny retinal vasculatures in retinal fundus images becomes difficult due to very low and varied contrast between the retinal vasculature and the backgroun...Show More

Abstract:

Analysis of the tiny retinal vasculatures in retinal fundus images becomes difficult due to very low and varied contrast between the retinal vasculature and the background. Fundus fluorescein angiogram overcomes these problems and provides an excellent visualization of the retinal vasculature; however it is an invasive procedure requiring injection of contrasting agents. Further investigation of the RETICA method, a non-invasive method of image enhancement developed earlier, is reported in this paper. It was found that noise is present in the Retinex image. Thus, the identification of the noise in the Retinex image and its removal has been the focus of this research paper. The method used to identify noise is based on adaptive wiener filters (additive, multiplicative, and additive plus multiplicative filters) and the fundus model image and real fundus images are applied to these filters. It is observed that retinal fundus images contained both additive and multiplicative noise. The noise is reduced by using adaptive wiener filter (additive plus multiplicative adaptive wiener filter) based method which resulted in 2.84db an improvement in PSNR.
Date of Conference: 29 November 2013 - 01 December 2013
Date Added to IEEE Xplore: 23 January 2014
ISBN Information:
Conference Location: Penang, Malaysia
References is not available for this document.

I. Introduction

Eye screening is vital in detection of diabetic retinopathy. There are five stages in diabetic retinopathy (DR) ranging from normal (No-DR), mild DR, moderate DR, severe non proliferative DR (NPDR) to PDR. The PDR stage is where there is a total loss of vision [1]. Haemorrhages, exudates and changes in the veins are some of the pathologies that, when present, characterise these DR classifications [2]. It was reported in a research carried out recently on the analysis of images of the fundus that as the severity level of DR advances, the fovea avascular zone size increases. The FAZ is observable in colour fundus images and in fundus fluorescein angiograms (FFA) [3].

Select All
1.
X. Zhitao, W. Jun, Z. Qian, and X. Jiangtao, Diabetic Retinopathy Fundus Image Processing Based on Phase Information, in Control, Automation and Systems Engineering (CASE), 2011 International Conference on, 2011, pp. 1-3.
2.
M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, et al., Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs, Medical Imaging, IEEE Transactions on, vol. 29, pp. 185-195, 2010.
3.
H. A. N. Ahmad Fadzil M Hani, Determination of Foveal Avascular Zone in Diabetic Retinopathy Digital Fundus Images, Journal of Computers in Biology and Medicine, 2010 Jul;40(7):657-64.
4.
M. H. A. Fadzil, H. A. Nugroho, P. A. Venkatachalam, H. Nugroho, and L. I. Izhar, Determination of Retinal Pigments from Fundus Images using Independent Component Analysis 4th Kuala Lumpur International Conference on Biomedical Engineering 2008. vol. 21, N. A. Abu Osman, F. Ibrahim, W. A. B. Wan Abas, H. S. Abdul Rahman, H.-N. Ting, and R. Magjarevic, Eds., ed: Springer Berlin Heidelberg, 2008, pp. 555-558.
5.
H. A. Nugroho, Non-Invasive Image Enhancement of Colour Retinal Fundus Image for Computerised Diabetic Retinopathy Monitoring and Grading System, Phd Thesis Electrical and Electronics Engineering Programme, Universiti Teknologi PETRONAS, 2012.
6.
A. F. M. Hani, T. Ahmed Soomro, H. Nugroho, and H. A. Nugroho, Enhancement of colour fundus image and FFA image using RETICA, in Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, 2012, pp. 831- 836.
7.
P. H. King, K. Hubner, W. Gibbs, and E. Holloway, Noise Identification and Removal in Positron Imaging Systems, Nuclear Science, IEEE Transactions on, vol. 28, pp. 148-151, 1981.
8.
Q. Yiwen, G. Zongliang, F. Yaqiong, and Z. Xiuchang, An adaptive image denoising method for mixture Gaussian noise, in Wireless Communications and Signal Processing (WCSP), 2011 International Conference on, 2011, pp. 1-5.
9.
Y. Lihong, Z. Xingxiang, and R. Jianyue, Adaptive wiener filtering with Gaussian fitted point spread function in image restoration, in Software Engineering and Service Science (ICSESS), 2011 IEEE 2nd International Conference on, 2011, pp. 890-894.
10.
C. Yixin and M. Das, An automated technique for image noise identification using a simple pattern classification approach, in Circuits and Systems, 2007. MWSCAS 2007. 50th Midwest Symposium on, 2007, pp. 819-822.
11.
S. Kollias, Y. Boutalis, and G. Carayannis, Fast adaptive identification and restoration of images degraded by blur and noise, in Circuits and Systems, 1988., IEEE International Symposium on, 1988, pp. 2073-2076 vol.3.
12.
K. Chehdi and M. Sabri, A new approach to identify the nature of the noise affecting an image, in Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on, 1992, pp. 285-288 vol.3.
13.
H. Kun, L. Xin-Cheng, L. Chun-Hua, and L. Ran, Gaussian Noise Removal of Image on the Local Feature, in Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on, 2008, pp. 867-871.
14.
S. Aja-Fernandez, R. S. J. Estepar, C. Alberola-Lopez, and C. F. Westin, Image Quality Assessment based on Local Variance, in Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE, 2006, pp. 4815-4818.
15.
J. M. Brian Funt, Florian Ciurea Retinex in MATLAB™, Journal of Electronic Imaging, pp. 48-57, 2004.
16.
A. a. O. Hyvarinen, E., Independent Component analysis: Alogrithms and applications,, Neural Networks, vol. 13, 2000.
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References

References is not available for this document.