Detection of Choroidal Neovascularization Through Characterization of Changes in Anterior and Posterior Eye Segments
- 28 Downloads
Choroidal neovascularization (CNV) is an age related eye disease caused due to the growth of new blood vessels beneath the retina which leads to vision loss. This is identified by analyzing the changes in the anterior segment such as filtration angle, which is one of the important parameters in ophthalmology and analyzing the posterior segment of the retinal image such as thickness of fusiform and commotion of blood vessels in Retinal Pigment Epithelium layer. Using image processing techniques, the Optical Coherence Tomography images of anterior and posterior eye segments are analyzed and the factors such as Angle opening distance, Euler number, thickness, angle of elevation and its maximum thickness value (in pixels) of the image are obtained. It is observed that there is a vast change in the above factors during CNV and thus it is possible to get knowledge whether the particular person is suffering from the disease or not. The factors obtained are fed to Neural Classifier which is used to show whether the person is in the starting stage or final stage of the disease.
KeywordsChoroidal neovascularization Optical coherence tomography Non invasive technique Anterior and posterior eye segments
The authors gratefully acknowledge the Vasan Eye Care, Chennai for providing the OCT images of macula as it was very helpful for analyzing the posterior eye segments. They also thank Robert Koprowski and Zygmunt Wrobel for giving permission to use the images from their book which were required to analyze the anterior eye segments.
- 4.Sasaki, K., Sakamoto, Y., & Shibata, T. (1990). Application of an anterior eye segment analysis system in clinical cataract research. Lens and Eye Toxicity Research, 7(3–4), 505–515.Google Scholar
- 5.Nowinska, A. K., Teper, S. J., Janiszewska, D. A., Lyssek-Boron, A., Dobrowolski, D., Koprowski, R., & Wylegala, E. (2015). Comparative study of anterior eye segment measurements with spectral swept-source and time-domain optical coherence tomography in eyes with corneal dystrophies. BioMed Research International, 2015, 805367. https://doi.org/10.1155/2015/805367.CrossRefGoogle Scholar
- 8.Prashanth, R., Paranjape, S. V., Ghosh, S., Dutta, P. K., & Chatterjee, J. (2010). Characterization of changes in retinal pigment epithelium layer in choroidal neovascularization through analysis of optical coherence tomographs. In Students’ technology symposium (TechSym), 2010 IEEE (pp. 39–43). IEEE.Google Scholar
- 9.Koprowski, R., & Wróbel, Z. (2011). Image processing in optical coherence tomography using Matlab. Gliwice: Robert Koprowski.Google Scholar
- 10.Takerkart, S., Fenouil, R., Piovano, J., Reynaud, A., Hoffart, L., Chavane, F., & Masson, G. S. (2008). A quantification framework for post-lesion neo-vascularization in retinal angiography. In 5th IEEE international symposium on biomedical imaging: From nano to macro, 2008. ISBI 2008 (pp. 1457–1460). IEEE.Google Scholar
- 11.Brankin, E., McCuIlagh, P., Black, N., Patton, W., & Muldrew, A. (2006). The optimisation of thresholding techniques for the identification of choroidal neovascular membranes in exudative age-related macular degeneration. In 19th IEEE International Symposium on Computer-Based Medical Systems, 2006. CBMS 2006 (pp. 430–435). IEEE.Google Scholar
- 12.Brankin, E., McCullagh, P., Patton, W., Muldrew, A., & Black, N. (2008). Identification of choroidal neovascularisation on fluorescein angiograms using gradient vector flow active contours. In International Machine Vision and Image Processing Conference, 2008. IMVIP’08 (pp. 165–169). IEEE.Google Scholar