A Survey on Retinal Area Detector for Classifying Retinal Disorders from SLO Images

Shobha Rani Baben, FISAT; Paul.P.Mathai ,FISAT

Feature selection, retinal artefacts extraction, retinal image analysis, scanning laser ophthalmoscope (SLO).

The retina is a dainty layer of tissue that lines the back of the eye within. It is situated close to the optic nerve. The reason for the retina is to get light that the focal point has centered, change over the light into neural flags, and send these signs on to the cerebrum for visual acknowledgment. The retina forms a photo from the engaged light, and the mind is left to choose what the photo is. Since the retina assumes crucial part in vision, harm to it can likewise bring about issues, for example, perpetual visual impairment. So we have to see if a retina is healthy or not for the early discovery of retinal sicknesses. Scanning laser ophthalmoscopes (SLOs) can be used for this purpose. The most recent screening innovation, gives the upside of utilizing SLO with its wide field of view, which can picture a vast part of the retina for better finding of the retinal ailments. On the opposite side, the artefacts, for example, eyelashes and eyelids are additionally imaged alongside the retinal zone, amid the imaging procedure. This delivers a major test on the best way to exclude these artefacts. The current systems has disadvantage that should be engaged. The proposed methodology includes feature extraction and feature matching techniques and construction of classifier to finally detect the retinal area and classifying the retina whether it’s healthy or not. The survey presents different techniques that are used to segment the artefacts and detect those artefacts.
    [1] Amit Madhukar Wagh , “Eyelids, Eyelashes Detection Algorithm and Hough Transform Method for Noise Removal in Iris Recognition ”, Biomedical Signal Processing and Control 25 (2016) 108117. [2] M.J.Aligholizadeh,S.Javadi,R.S.Nadooshan, and K.Kangarloo, “Eyelid and eye lash segmentation based on wavelet transform for iris recognition”, in Proc.4thInt.Congr.ImageSignalProcess. 2011, pp.12311235. [3] A.V.Mire and B.L.Dhote, “Iris Recognition System with Accurate Eyelash Segmentation Improved FAR, FRR using textural Topological Features”, IEEE Trans.Med. Imaging, vol. 7, pp. 09758887, 2010. [4] D. Zhang, D. Monro, and S. Rakshit, “Eyelash removal method for human iris recognition”,in Proc. IEEE Int. Conf. Image Process., 2006, pp. 285288. [5] J. Xu, O. Chutatape, P. Chew, “Human Iris Segmentation for Iris Recognition in Unconstrained Environments ”, nternational Journal of Computer Science Issues Vol. 9, Issue 1, No 3, January 2012 473482. [6] Y.-H.Li,M.Savvides,and T.Chen, “Investigating useful and distinguishing features around the eyelash region”,in Proc. 37th IEEE Workshop Appl. Imag. Pattern Recog. 2008, pp. 16. [7] H. Yu, C. Agurto, S. Barriga, S. C. Nemeth, P. Soliz, and G. Zamora “Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening”, Proc. IEEE Southwest Symp. Image Anal. Interpretation, 2012, pp. 125128. [8] J. A. M. P. Dias, C. M. Oliveira, and L. A. d. S. Cruz“Retinal image quality assessment using generic image quality indicators”, IEEE Trans. Inf. Technol.Biomed. 16 (4) (2012) 644657. [9] Suraya Mohammad, et al., “Texture analysis for the segmentation of optic disc inretinal images”, in: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, pp. 42654270. [10] R.Pires, H.Jelinek,J.Wainer ,and A.Rocha., “Retinal image quality analysis for automatic diabetic retinopathy detection”, in: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, pp. 42654270.
Paper ID: GRDJEV01I100021
Published in: Volume : 1, Issue : 10
Publication Date: 2016-10-01
Page(s): 54 - 58