title: A new blood vessel extraction technique using edge enhancement and object classification.
abstract: Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.
authors.affiliation: Faculty of Engineering, Department of Electrical Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
authors.forename: Shahriar
authors.initials: S
authors.lastname: Badsha
authors.forename: Ahmed Wasif
authors.initials: AW
authors.lastname: Reza
authors.forename: Kim Geok
authors.initials: KG
authors.lastname: Tan
authors.forename: Kaharudin
authors.initials: K
authors.lastname: Dimyati
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D012171:Retinal Vessels / Q000473:pathology*; D042262:Retinoscopy / Q000379:methods; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index