Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Classification and Localisation of Diabetic-Related Eye Disease
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
An Automatic System for the Location of the Optic Nerve Head from 2D Images
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Localisation of the optic disc by means of GA-optimised Topological Active Nets
Image and Vision Computing
Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach
Computers in Biology and Medicine
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
A morphologic two-stage approach for automated optic disk detection in color eye fundus images
Pattern Recognition Letters
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Objective: This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. Methods and material: Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels. Next, a genetic algorithm was used to find an ellipse containing the maximum number of hypothesis points in an offset of its perimeter, considering some constraints. The ellipse thus obtained is the approximation to the ONH. The segmentation method is tested in a sample of 110 eye fundus images, belonging to 55 patients with glaucoma (23.1%) and eye hypertension (76.9%) and random selected from an eye fundus image base belonging to the Ophthalmology Service at Miguel Servet Hospital, Saragossa (Spain). Results and conclusions: The results obtained are competitive with those in the literature. The method's generalization capability is reinforced when it is applied to a different image base from the one used in our study and a discrepancy curve is obtained very similar to the one obtained in our image base. In addition, the robustness of the method proposed can be seen in the high percentage of images obtained with a discrepancy @d