An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection.
This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.
Saleh
Marwan D
MD
Centre for Communication Infrastructure, Faculty of Information Technology, Multimedia University, Jalan Multimedia, Cyberjaya, Selangor, Malaysia. marwan.d.saleh06@mmu.edu.my
Eswaran
C
C
Mueen
Ahmed
A
D000465:Algorithms; D016208:Databases, Factual; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography; D015203:Reproducibility of Results; D012171:Retinal Vessels / Q000033:anatomy & histology*