title: Improvement of a retinal blood vessel segmentation method using the Insight Segmentation and Registration Toolkit (ITK).
abstract: We describe an improved implementation of a segmentation method for retinal blood vessels based on a multi-scale approach and region growing employing modules from the Insight Segmentation and Registration Toolkit (ITK). We present the results of segmentation of retinal blood vessels using this improved method and compare these with results obtained using the original implementation in Matlab, as well as with expert manual segmentations obtained from a public database. We show that the ITK implementation achieves high quality segmentations with markedly improved computational efficiency. The ITK version has greater segmentation accuracy, from 0.94 to 0.96, than the Matlab version due to a decrease in FPR values and it is between 8 and 12 times faster than the original version. Furthermore, the ITK implementation is able to segment high-resolution images in an acceptable timescale.
authors.affiliation: Department of Computer Science. Institute of Research in Applied Mathematics and Systems. UNAM, Apdo. Postal 20-726, México, D.F. 04510. elena@leibniz.iimas.unam.mx
authors.forename: M
authors.initials: M
authors.lastname: Martinez-Perez
authors.forename: Alun D
authors.initials: AD
authors.lastname: Hughes
authors.forename: Simon A
authors.initials: SA
authors.lastname: Thom
authors.forename: Kim H
authors.initials: KH
authors.lastname: Parker
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011381:Programming Languages; D015203:Reproducibility of Results; D012171:Retinal Vessels / Q000473:pathology*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software; D013382:Subtraction Technique