An ensemble classification-based approach applied to retinal blood vessel segmentation.
This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.
Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University London, Surrey, UK. moazam. fraz@kingston.ac.uk
Muhammad Moazam
MM
Fraz
Paolo
P
Remagnino
Andreas
A
Hoppe
Bunyarit
B
Uyyanonvara
Alicja R
AR
Rudnicka
Christopher G
CG
Owen
Sarah A
SA
Barman
D000465:Algorithms; D019540:Area Under Curve; D002648:Child; D016208:Databases, Factual; D003663:Decision Trees; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012171:Retinal Vessels / Q000033:anatomy & histology*