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.
|Titolo:||Diabetic Damage Detection in Retinal Images Via a Sparsely-Connected Neurofuzzy Network|
|Titolo del libro:||Advanced intelligent computing theories and applications : with aspects of artificial intelligence|
|Data di pubblicazione:||2008|
|Digital Object Identifier (DOI):||10.107/978-3-540-85984-0_141|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|