Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction. Issue 7 (5th January 2021)

• Record Type: Journal Article
• Authors: Tavakoli, Meysam; Mehdizadeh, Alireza; Pourreza Shahri, Reza; Dehmeshki, Jamshid
• Is Part Of: IET image processing. Volume 15:Issue 7(2021)
• Page Start: 1484
• Page End: 1498
• Publication Date: 2021-01-05
• Subjects: Physiological optics, vision -- Haemodynamics, pneumodynamics -- Optical and laser radiation (medical uses) -- Patient diagnostic methods and instrumentation -- Integral transforms -- Optical, image and video signal processing -- Optical and laser radiation (biomedical imaging/measurement) -- Integral transforms -- Computer vision and image processing techniques -- Biology and medical computing -- Unsupervised learning; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12119
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Retinal blood vessel segmentation and analysis is critical for the computer‐aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method for segmenting the retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance the vessel tree structure against a non‐uniform image background. The main processing applies the Radon transform to overlapping windows, followed by vessel validation, vessel refinement and vessel reconstruction to achieve the final segmentation. The method was tested on three publicly available datasets and a local database comprising a total of 188 images. Segmentation performance was evaluated using three measures: accuracy, receiver operating characteristic (ROC) analysis, and the structural similarity index. ROC analysis resulted in area under curve values of 97.39%, 97.01%, and 97.12%, for the DRIVE, STARE, and CHASE‐DB1, respectively. Also, the results of accuracy were 0.9688, 0.9646, and 0.9475 for the same datasets. Finally, the average values of structural similarity index were computed for all four datasets, with average values of 0.9650 (DRIVE), 0.9641 (STARE), and 0.9625 (CHASE‐DB1). These results compare with the best published results to date, exceeding their performance for several of the datasets; similar performance is found using accuracy.