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M. I. Iqbal, A. M. Aibinu, I. B. Tijani, M. Nilsson and M. J. E. Salami, “Cross Point Detection Using Fuzzy Logic and Neural Network,” Proceedings of the International Conference on Computer and Communication Engineering, Kuala Lumpur, 13-15 May 2008, pp. 241-246.

has been cited by the following article:

• TITLE: Using Genetic Algorithm for Identification of Diabetic Retinal Exudates in Digital Color Images

  AUTHORS: Romany Fouad Mansour

  KEYWORDS: Diabetic Retinal; Genetic Algorithm; Chain Code; Vessel Detection; Fundus Image

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.3, August 30, 2012

  ABSTRACT: Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathology on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. In this paper, a new hybrid approach called the (Genetic algorithm and vertex chain code) for blood vessel detection. And this method uses geometrical parameters of retinal vascular tree for diagnosing of hypertension and identified retinal exudates automatically from color retinal images. The skeletons of the segmented trees are produced by thinning. Three types of landmarks in the skeleton must be detected: terminal points, bifurcation and crossing points, these points are labeled and stored as a chain code. Results of the proposed system can achieve a diagnostic accuracy with 96.0% sensitivity and 98.4% specificity for the identification of images containing any evidence of retinopathy.