In this paper a contribution towards diabetic damage detection in retinal images is proposed by synthesizing a Sparsely Connected Neurofuzzy Network for fundus image processing in the presence of retinopathies. A Hopfield-like neurofuzzy subnetwork is firstly synthesized to obtain contrast-enhanced images. After an optimal thresholding performed by an MLP-based neural subsystem, contrast-enhanced images are then globally segmented by a further sparsely-connected neural subnet to highlight vague pale regions. In this way diabetic damaged areas reveal isolated in bipolar output images. Experimental cases are reported and discussed.
Diabetic Damage Detection in Retinal Images Via a Sparsely-Connected Neurofuzzy Network / Carnimeo, Leonarda (LECTURE NOTES IN COMPUTER SCIENCE). - In: Advanced intelligent computing theories and applications : with aspects of artificial intelligence / [a cura di] Huang, DS; Wunsch, DC; Levine, DS; Jo, KH. - STAMPA. - Berlin; Heidelberg : Springer, 2008. - ISBN 978-3-540-85983-3. - pp. 1175-1182 [10.1007/978-3-540-85984-0_141]
Diabetic Damage Detection in Retinal Images Via a Sparsely-Connected Neurofuzzy Network
CARNIMEO, Leonarda
2008-01-01
Abstract
In this paper a contribution towards diabetic damage detection in retinal images is proposed by synthesizing a Sparsely Connected Neurofuzzy Network for fundus image processing in the presence of retinopathies. A Hopfield-like neurofuzzy subnetwork is firstly synthesized to obtain contrast-enhanced images. After an optimal thresholding performed by an MLP-based neural subsystem, contrast-enhanced images are then globally segmented by a further sparsely-connected neural subnet to highlight vague pale regions. In this way diabetic damaged areas reveal isolated in bipolar output images. Experimental cases are reported and discussed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
