title: An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding.
abstract: Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.
authors.affiliation: Centre for Communication Infrastructure, Faculty of Information Technology, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor, Malaysia. marwan_salih@yahoo.com
authors.forename: Marwan D
authors.initials: MD
authors.lastname: Saleh
authors.forename: C
authors.initials: C
authors.lastname: Eswaran
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003930:Diabetic Retinopathy / Q000175:diagnosis / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D005451:Fluorescein Angiography / Q000706:statistics & numerical data; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D012171:Retinal Vessels / Q000473:pathology*