In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.
Segmentation of vasculature in retinal fundus image by level set methods employing classical edge detection methodologies is a tedious task. In this study, a revised level set-based retinal vasculature segmentation approach is proposed. During preprocessing, intensity inhomogeneity on the green channel of input image is corrected by utilizing all image channels, generating more efficient results compared to methods utilizing only one (green) channel. A structure-based level set method employing a modified phase map is introduced to obtain accurate skeletonization and segmentation of the retinal vasculature. The seed points around vessels are selected and the level sets are initialized automatically. Furthermore, the proposed method introduces an improved zero-level contour regularization term which is more appropriate than the ones introduced by other methods for vasculature structures. We conducted the experiments on our own dataset, as well as two publicly available datasets. The results show that the proposed method segments retinal vessels accurately and its performance is comparable to state-of-the-art supervised/unsupervised segmentation techniques.
Purpose To examine vascular tortuosity as a function of distance from the optic disc in infants with retinopathy of prematurity (ROP). Methods 34 wide-angle retinal images from infants with ROP were reviewed by 22 experts. A reference standard for each image was defined as the diagnosis (plus vs. not plus) given by the majority of experts. Tortuosity, defined as vessel length divided by straight-line distance between vessel endpoints, was calculated as a function of distance from the disc margin for arteries and veins using computer-based methods developed by the authors. Results Mean cumulative tortuosity increased with distance from the disc margin, both in 13 images with plus disease (p=0.007 for arterial tortuosity (n=62 arteries), p<0.001 for venous tortuosity (n=58 veins) based on slope of best fit line by regression), and in 21 images without plus disease (p<0.001 for arterial tortuosity (n=94 arteries), p<0.001 for venous tortuosity (n=85 veins)). Images with plus disease had significantly higher vascular tortuosity than images without plus disease (p<0.05), up to 7.0 disc diameters from the optic disc margin. Conclusions Vascular tortuosity was higher peripherally than centrally, both in images with and without plus disease, suggesting that peripheral retinal features may be relevant for ROP diagnosis.
Purpose: To provide normative data of full-field electro-retinogram (ERG) responses in the pediatric population using the RETeval ERG device (LKC Technologies, Inc) in healthy children without evidence of retinal disease. Methods: This was a single-site cross-sectional study of healthy pediatric patients with normal dilated fundus examinations and no known retinal diseases. Participants were recruited to undergo dilated full-field ERG using the handheld RETeval device. The International Society for Clinical Electrophysiology of Vision 5-step protocol was used. Photopic and dark-adapted scotopic responses were recorded using skin electrodes. Results: Main outcome measures were normative RETeval ERG values and correlation of age with measured ERG parameters. Thirty-eight eyes of 20 healthy patients (aged 4 to 17 years) were included in the study. Of the 20 normal patients, 9 were male and 11 were female. Normative mean, median, and range values were recorded for the measured full-field ERG parameters. Pearson correlation was moderately positive between age and oscillatory potential and scotopic dim flash amplitude ( r = 0.59, P = .006 for both). A positive correlation was also found between age and cone a-wave implicit time ( r = 0.67, P = .001). Conclusions: The handheld RETeval system is a useful tool for obtaining full-field ERGs in children without anesthesia. Moderate positive correlations were observed between age and oscillatory potential and scotopic dim flash amplitude. A strong correlation was found between age and cone a-wave implicit time. The current study provides a baseline of normative full-field ERG values in children. [ J Pediatr Ophthalmol Strabismus . 2021;58(1):17–22.]
SummaryObjective: Inter-expert variability in imagebased clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. Methods:The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer
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