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

fYear

2006

fDate

Nov. 29 2006-Dec. 1 2006

Firstpage

138

Lastpage

141

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/BIOCAS.2006.4600327

Filename

4600327