Identification of noise in the fundus images

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.

[1]  Lila Iznita Izhar,et al.  Determination of foveal avascular zone in diabetic retinopathy digital fundus images , 2010, Comput. Biol. Medicine.

[2]  John J. McCann,et al.  Retinex in MATLABTM , 2004, J. Electronic Imaging.

[3]  Santiago Aja-FernRa,et al.  Image Quality Assessment based on Local Variance , 2006 .

[4]  Jianyue Ren,et al.  Adaptive wiener filtering with Gaussian fitted point spread function in image restoration , 2011, 2011 IEEE 2nd International Conference on Software Engineering and Service Science.

[5]  L. Tarassenko,et al.  Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE , 2014 .

[6]  Stefanos Kollias,et al.  Fast adaptive identification and restoration of images degraded by blur and noise , 1988, 1988., IEEE International Symposium on Circuits and Systems.

[7]  K. Chehdi,et al.  A new approach to identify the nature of the noise affecting an image , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  John J. McCann,et al.  Retinex in Matlab , 2000, CIC.

[9]  P. King,et al.  Noise Identification and Removal in Positron Imaging Systems , 1981, IEEE Transactions on Nuclear Science.

[10]  Zongliang Gan,et al.  An adaptive image denoising method for mixture Gaussian noise , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[11]  Toufique Ahmed Soomro,et al.  Enhancement of colour fundus image and FFA image using RETICA , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[12]  J. Xi,et al.  Diabetic Retinopathy Fundus Image Processing Based on Phase Information , 2011, 2011 International Conference on Control, Automation and Systems Engineering (CASE).

[13]  Lila Iznita Izhar,et al.  Determination of Retinal Pigments from Fundus Images using Independent Component Analysis , 2008 .

[14]  Zhou Ji-liu.  Gaussian Noise Removal on the Local Feature of Image , 2010 .

[15]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[16]  Yixin Chen,et al.  An automated technique for image noise identification using a simple pattern classification approach , 2007, 2007 50th Midwest Symposium on Circuits and Systems.

[17]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.