Fuzzy Neural Network Classification of all True Vessels from Segmented Retinal Images Princye P. Hosanna*, Dr. Kumari V. Vijaya** *Adhiyamaan College of Engineering, Hosur, India **Professor, Sri Krishna College of Technology, Coimbatore, India Online published on 15 September, 2016. Abstract In general, the risk on recognizing the Cardio Vascular (CV) is made with the retinal blood vessel tracking. If the wrong blood vessel is identified, then it may lead to an incorrect clinical diagnosis. In the work already available, the segmented vascular structure is modeled in the form of a vessel segment graph and then the problem of identification of the vessels is formulated. But in this technique, the vessel segmentation is carried out with the Optic Disc (OD) not being eliminated. As the OD remains as the converging point for all the vessels present inside the retina, the OD boundary elimination results in few of the visible blood vessels cross right through it. Therefore, the OD boundary has to be eliminated prior to the segmentation of the vessels, since it may cause OD pixels misdetection that overlap with the blood vessels. In this work, an automated segmentation and vessel segmentation based elimination of OD boundary is introduced to prevent such kind of misdetections that improve the accuracy further in the case of vessel detection. The proposed technique has made use of modules like pre-processing, segmentation and feature extraction. This paper presents a preprocessing step applying Kalman filtering for the purpose of the noise removal and normalization for achieving image enhancement. Optical disc segmentation step employs Discrete Anisotropic Filter (DAF) with Particle Swarm Optimization (PSO). Thereafter some of the true vessels (CV severity) are identified from the segmented images by means of Fuzzy Neural Network (FNN). The results obtained from experiments demonstrate that this algorithm proposed has been proven to be a highly efficient technique for the classification of retinal blood vessels and the average accuracy attained is 97.2%. Top Keywords Cardio vascular, retinal image analysis, true vessel identification, FNN classification, PSO, Kalman filtering. Top |