Title :
Diabetic damage detection in retinal images via a cellular neurofuzzy network
Author :
Carnimeo, Leonarda ; Giaquinto, Antonio
Author_Institution :
Dipt. di Elettrotec. ed Elettron., Politec. di Bari, Bari
fDate :
Nov. 29 2006-Dec. 1 2006
Abstract :
In this paper a contribution to diabetic damage detection in retinal images via a cellular neurofuzzy network is proposed. Fundus symptomatic pale regions are firstly highlighted by enhancing image contrast with a neurofuzzy subnet, which is synthesized using a Cellular Neural Network (CNN). After an optimal thresholding performed by a neural subsystem, obtained contrast-enhanced images with bimodal histograms are globally segmented. In output binary images, suspect diabetic areas are then isolated by a CNN-based subnet. Performances are evaluated by percentage measures of exactness in the detection of suspect damaged areas via a comparison with gold standard images provided by clinicians. Results are discussed and compared with other researcherspsila ones.
Keywords :
diseases; eye; fuzzy neural nets; image enhancement; image segmentation; medical image processing; bimodal histograms; cellular neural network; cellular neurofuzzy network; diabetic damage detection; fundus symptomatic pale regions; image contrast enhancement; image segmentation; retinal images; Area measurement; Cellular networks; Cellular neural networks; Diabetes; Gold; Histograms; Image segmentation; Network synthesis; Performance evaluation; Retina;
Conference_Titel :
Biomedical Circuits and Systems Conference, 2006. BioCAS 2006. IEEE
Conference_Location :
London
Print_ISBN :
978-1-4244-0436-0
Electronic_ISBN :
978-1-4244-0437-7
DOI :
10.1109/BIOCAS.2006.4600327
