Affiliation:
1. School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan
Abstract
Segmentation of the retinal blood vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic retinal image analysis. This paper optimized the blood vessel segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for vessel segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value ¡ 0.05). The proposed enhancement has improved the vessel segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively.
Subject
General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience
Reference47 articles.
1. Retinal imaging and image analysis;Abràmoff;IEEE Reviews in Biomedical Engineering,2010
2. An active contour model for segmenting and measuring retinal vessels;Al-Diri;IEEE Transactions on Medical imaging,2009
3. Increased generalization capability of trainable cosfire filters with application to machine vision;Azzopardi,2016
4. Gender recognition from face images with trainable COSFIRE filters;Azzopardi,2016
5. A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model;Azzopardi;Biological Cybernetics,2012
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献