Retinal Blood Vessel Segmentation Using Clustering Mathematical Morphology And Thresholding Techniques

Abstract

Fundus imaging is a particular field of biomedical image processing, where we can newlinedirectly envisage the pathology and treatment accordingly. It helps in verifying the newlinedisease process which further assists in segmenting, treating and following up newlinesuccessfully. Ophthalmology helps in diagnosing many ocular and systemic diseases newlinesuch as diabetic retinopathy, hypertension, glaucoma, subacute bacterial endocarditis, newlineleukemia, systemic malignancy with ocular metastasis etc. and thus helps in effectively newlinehandling the illness. Thus, the doctoral thesis aims to devloped different techniques for newlineeffective and accurate retinall blood vessels identification. Thel segmentation performance newlineis evaluatedl both quantitatively and qualitatively for digital retinall imageslfor newlinevessellextractionl (DRIVEk) and ChildlHeartl and HealthlStudy inlEnglandl database newline(CHASE_DB1m) databases respectively. The quantitative evaluation is done via eight newlineperformance matrices as SEN, SPE, PPV, NPV, FDR, D, A and and#119905;and#119901;. For qualitative newlineevaluation, the segmentation outputs of devloped techniques are compared with the newlineground truths. newline