Identification of the optic nerve head with genetic algorithms
Artificial Intelligence in Medicine
Topological active nets optimization using genetic algorithms
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Evolutionary multiobjective optimization of Topological Active Nets
Pattern Recognition Letters
Robust optic disk segmentation from colour retinal images
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Multiobjective differential evolution in the optimization of topological active models
Applied Soft Computing
Extended Topological Active Nets
Image and Vision Computing
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In this paper we propose a new approach to the optic disc localisation process in digital retinal images by means of Topological Active Nets (TAN). This is a deformable model used for image segmentation that integrates features of region-based and edge-based segmentation techniques, being able to fit the edges of the objects and model their inner topology. In this paper the active nets incorporate new energy terms for the optic disc localisation and their optimisation is performed with a genetic algorithm, with adapted or new ad hoc genetic operators. There is no need of any pre-processing of the images, which allows a quasi automatic localisation of the optic disc. This process also provides a simultaneous segmentation of the disc. We present representative results of optic disc localisations showing the advantages of the approach, with images focusing on the optic disc or on the macula, and with images with different levels of noise and lesion areas.