Abstract
In this work, we present a rules-based method for localizing retinal blood vessels in confocal scanning laser ophthalmoscopy (cSLO) images and evaluate its feasibility. A total of 31 healthy participants (17 female; mean age: 64.0 ± 8.2 years) were studied using manual and automatic segmentation. High-resolution peripapillary scan acquisition cSLO images were acquired. The automated segmentation method consisted of image pre-processing for gray-level homogenization and blood vessel enhancement (morphological opening operation, Gaussian filter, morphological Top-Hat transformation), binary thresholding (entropy-based thresholding operation), and removal of falsely detected isolated vessel pixels. The proposed algorithm was first tested on the publically available dataset DRIVE, which contains color fundus photographs, and compared to performance results from the literature. Good results were obtained. Monochromatic cSLO images segmented using the proposed method were compared to those manually segmented by two independent observers. For the algorithm, a sensitivity of 0.7542, specificity of 0.8607, and accuracy of 0.8508 were obtained. For the two independent observers, a sensitivity of 0.6579, specificity of 0.9699, and accuracy of 0.9401 were obtained. The results demonstrate that it is possible to localize vessels in monochromatic cSLO images of the retina using a rules-based approach. The performance results are inferior to those obtained using fundus photography, which could be due to the nature of the technology.
Highlights
The segmentation of retinal vessels and their morphology such as length, width, tortuosity, and branching patterns can be used for the diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases [1]
In this work, we present a rules-based method for localizing retinal blood vessels in confocal scanning laser ophthalmoscopy images and evaluate its feasibility
2 Department of Computer Science, University of Manitoba, Winnipeg, Canada results are inferior to those obtained using fundus photography, which could be due to the nature of the technology
Summary
The segmentation of retinal vessels and their morphology such as length, width, tortuosity, and branching patterns can be used for the diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases [1]. Automatic detection of retinal vessels and the analysis of their morphology are feasible in screening programs for diabetic retinopathy [4], arteriolar narrowing detection [5], detection of foveal avascular regions [6], retinopathy of prematurity evaluation [7], and investigation of general cardiovascular diseases and hypertension [8]. Temporal or multimodal image registration [9], optic disc identification, and fovea localization [10] are possible using automatic algorithms for retinal vessel detection and branch point detection. The automated analysis of the above-mentioned morphological features is accepted by the medical community [13], as manual measurement is time-consuming, and dependent on the observer and their experience
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