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.
Highlights
The analysis of shape, appearance, tortuosity and other morphological characteristics of blood vessels in human retinal images are critical diagnostic indicators to eye diseases
The empirical experiment is performed on each dataset training images like (DRIVE, STARE, and CHASE-DB1) using the parameter optimization
As p-value = 0.000 ≤ 0.05 only for Specificity threshold parameter is only significant for Specificity in DRIVE DB Insignificant, as p-value > 0.05
Summary
The analysis of shape, appearance, tortuosity and other morphological characteristics of blood vessels in human retinal images are critical diagnostic indicators to eye diseases. Accurate segmentation of retinal blood vessels can be considered as the first step in the development of an automated system for diagnostic retinal image analysis. In the retinal image captured from fundus camera, the blood vessels emerge from retinal Optic Disc with their branches spread across the retina. The retinal vasculature is comprised of arterioles and venules, appears to be piecewise line-shaped and its width is variable across the vessel length (Abràmoff, Garvin & Sonka, 2010). Optimized results Specificity Dataset DRIVE STARE CHASEDB1. Th0e.8c1o3mparison0.o96f5the optim0i.z9e52d results with 262 other met2hods is d0i.s4cussed. 30
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