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Automatic Characterization of Choroidal Neovascularization Lesions in Fluorescein Angiograms using Parametric Modeling

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IJCA Proceedings on National Conference on Computational Intelligence for Engineering Quality Software
© 2014 by IJCA Journal
CiQS - Number 1
Year of Publication: 2014
Authors:
G. Anidha
U. Sabura Banu
J. Mohamed Thowfiq Raja

G.anidha, U.sabura Banu and J.mohamed Thowfiq Raja. Article: Automatic Characterization of Choroidal Neovascularization Lesions in Fluorescein Angiograms using Parametric Modeling. IJCA Proceedings on National Conference on Computational Intelligence for Engineering Quality Software CiQS(1):25-28, October 2014. Full text available. BibTeX

@article{key:article,
	author = {G.anidha and U.sabura Banu and J.mohamed Thowfiq Raja},
	title = {Article: Automatic Characterization of Choroidal Neovascularization Lesions in Fluorescein Angiograms using Parametric Modeling},
	journal = {IJCA Proceedings on National Conference on Computational Intelligence for Engineering Quality Software},
	year = {2014},
	volume = {CiQS},
	number = {1},
	pages = {25-28},
	month = {October},
	note = {Full text available}
}

Abstract

In many older adults, it is very common to observe Age related Macular degeneration which ultimately results in a medical condition called Choroidal Neo-Vascularization (CNV). It is characterized by having loss of linearity in the retinal image, fusiform thickening, and disruption of blood vessels in Retinal Pigment Epithelium (RPE) layer and thus results in vision loss. The images obtained from fluorescein angiography are further processed using digital image processing techniques and the results are obtained stating whether the person is suffering from the disease or not. Using parametric modeling of the intensity variation, the factors such as peak amount of the fluorescein that accumulates in the early stage, middle stage and late stage of the images are obtained. The end result which are the parameters obtained from the images, are fed to a Neural classifier. It classifies the quantized image parameters into occult CNV and classical CNV. This will tend to show whether the particular disease is dangerous and in the final stage or in the starting stage.

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