Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

IET Image Processing

卷:15

Alireza Mehdizadeh¹  Reza Pourreza Shahri²  Meysam Tavakoli³  Jamshid Dehmeshki⁴ 

[1] Department of Biomedical Physics and Engineering Shiraz University of Medical Sciences Shiraz Iran;

[2] Department of Electrical Engineering University of Texas at Dallas Dallas TX USA;

[3] Department of Physics Indiana University‐Purdue University Indianapolis IN USA;

[4] Quantitative Medical Imaging Centre (QMIC) Faculty of Science Engineering and Computing Kingston University London UK;

关键词: Physiological optics, vision;    Haemodynamics, pneumodynamics;    Optical and laser radiation (medical uses);    Patient diagnostic methods and instrumentation;    Integral transforms;    Optical, image and video signal processing;   

DOI  :  10.1049/ipr2.12119

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown