Email this article 
Open Access
Subscription or Fee Access
A Thorough Investigation on Performance Analysis of Retinal Image Processing Techniques
Abstract
The detection of blood vessels from the retinal images is generally a slow process. Also, it is used to detect the diabetes in early stages by evaluating all the retinal blood vessels together. As digital imaging and computing power increasingly develop, so these technologies are used in ophthalmology. The main divisions include Image processing, Image analysis and Computer vision are increasingly becoming more prominence in all fields of medical science, and they are especially suited to the field of modern ophthalmology. Exciting developments in image processing relevant to ophthalmology over the past 15 years includes the progress being made towards developing a good solution for diseases such as diabetic retinopathy, age-related macular degeneration and retinopathy of prematurity. The quantitative measurements of retinal vascular topography using digital image analysis from retinal photography have been used as research tools to better understand the relationship between the retinal microvasculature and cardiovascular disease. Furthermore, advances in electronic media transmission increase the relevance of using image processing as an aid in clinical decision-making, with particular relevance to large rural-based communities. This paper mainly focuses on the phases involved in Retinal image processing and its concepts that can be more useful for the researcher’s to understand the basics.
Keywords
References
Akita, K.,Kuga, H.,1982. A computer method of understanding ocular fundus images. Pattern Recogn. 16,431–443.
Aldington,S.,Kohne r,E.,Meuer, S.,Klein, R.,Sjolie, A.,1995. Methodology for retinal photography and assessment of diabetic retinopathy—the EURODIAB IDDM complications study. Diabetologia 38,437–444.
Amos,A., McCarty, D.,Zimmet,P.,1997. The rising global burden of diabetes and itscomplications: estimates and projections to the year 2010. Diabet. Med. 14,S7–S13.
Arnold,J.V., Gates,J.W.C.,Taylor, K.M.,1993. Possible errors in the measurement of retinal lesions. Invest. Ophthalmol. Vis. Sci. 34, 2576–2580.
Arzabe,C., Jalkh,A.,Fariza,E.,Akiba,J., Quiroz,M.,1990. A simple device to standardize measurements of retinal structures in fundus photographs and retinal angiograms. Am. J. Ophthalmol. 109, 107–108.
Avakian, A.,Kalina,R.,Sage,E.,Rambhia, A.,Elliott,K.,Chuang, E.,Clark, J.,Hwang, J.-N., Parsons-Wingerter,P., 2002. Fractal analysis of region-based vascular change in the normal and nonproliferative diabetic retina. Curr. Eye Res. 24,274–280.
Baer,R., Hill,D.,1990. Retinal vessel responses to passive tilting. Eye 4,751–756.
Barthes,A., Conrath,J., Rasigni,M., Adel, M.,Petrakian,J.,2001. Mathematical morphology in computerized analysis of angiograms in age-related macular degeneration. Med. Phys. 28, 2410–2419.
Basu,A.,Kamal,A.,Illahi,W., Stavrou, P.,Ryder,R.,2003. Is digital image compression acceptable within diabetic retinopathy screening.Diabet. Med. 20,766–771.
Behrendt,T.,Doyle,K., 1965. Reliability of image size measurements in the new Zeiss fundus camera. Am. J. Ophthalmol. 59,896–899.
Bellmann,C., Rubin, G.,Kabanarou ,S.,Bird, A., Fizke, F.,2003. Fundus autofluorescence imaging compared with different confocal scanning laser ophthalmoscopes. Br. J. Ophthalmol. 87,1381–1386.
Bengtsson,B.,Krakau,C., 1992. Correction of optic disc measurements on fundus photographs. Graefe’s Arch. Clin. Exp. Ophthalmol. 230,24–28.
Bennett, A.,Rudnicka, A.,Edgar, D.,1994. Improvements on Littman’s method of determining the size of retinal features by fundus photography. Graefe’s Arch. Clin. Exp. Ophthalmol. 232, 361–367.
Capowski,J.,Klystra, J.,Freedm an,S.,1995. A numerical index based on spatial frequency for the tortuosity of retinal vessels and its application to plus disease in retinopathy of prematurity. Retina 15,490–500.
Chapman,N.,Haimes, G.,Stanton, A.,Thom,S.,Hughes,A.,2000. Acute effects of oxygen and carbon dioxide on retinal vascular network geometry in hypertensive and normotensive subjects. Clin. Sci. 99,483–488.
Chapman,N.,Witt, N.,Gao,X.,Bharath, A.A., Stanton, A.V., Thom,S.A.,Hughes,A.D.,2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. Br. J. Ophthalmol. 85,74–79.
Chapman,N.,Dell’omo,G.,Sartini, M.S., Witt,N.,Hughes,A., Thom,S., Pedrinelli,R., 2002. Peripheral vascular disease is associated with abnormal arteriolar diameter relationships at bifurcations in the human retina. Clin. Sci. 103,111–116.
Chaudhuri, S.,Chatterjee,S.,Katz,N., Nelson, M.,Goldbaum ,M., 1989a. Automatic detection of the optic nerve in retinal images. In: Proceedings of the IEEE International Conference on Image Processing,vol. 1. Singapore, pp. 1–5.
Chaudhuri, S., Chatterjee,S.,Katz,N., Nelson, M.,Goldbaum ,M., 1989b. Detection of retinal blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag. 8, 263–369.
Niall Pattona,b,_,Tariq M. Aslamc,ThomasMacGillivrayd,Ian J. Dearye, BaljeanDhillonb,Robert H. Eikelboomf,g,KanagasingamYogesana,Ian J. Constablea. Retinal image analysis: Concepts,applications and potential. Elsevier publications
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.