Ab stract-In medical images linear patterns such as blood vessels are important structures for computer-aided diagnosis and follow-up of many diseases. Moreover, image processing techniques are required to extract suitable information about vascular tree and its alteration. Analyzing of retinal blood vessel is critical work for the investigation of some diseases. In this study, we present an automated method for detecting retinal vasculatures based upon Radon transform. In preprocessing, we used top-hat transformation and averaging filter. Our main processing was included applying Radon transform, vessel certifying, and vessel refinement. Comparing the results of our method with gold standard showed that our results have more than 93% for true positive rate. In conclusion, it is possible to use Radon transform for vessel segmentation in fluorescein angiography fundus images, with acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.
Purpose:To compare the distribution of different sized vessels using digital photographs of the ocular surface of diabetic and normal individuals.Methods:In this cross-sectional study, red-free conjunctival photographs of diabetic and normal individuals, aged 30-60 years, were taken under defined conditions and analyzed using a Radon transform-based algorithm for vascular segmentation. The image areas occupied by vessels (AOV) of different diameters were calculated. The main outcome measure was the distribution curve of mean AOV of different sized vessels. Secondary outcome measures included total AOV and standard deviation (SD) of AOV of different sized vessels.Results:Two hundred and sixty-eight diabetic patients and 297 normal (control) individuals were included, differing in age (45.50 ± 5.19 vs. 40.38 ± 6.19 years, P < 0.001), systolic (126.37 ± 20.25 vs. 119.21 ± 15.81 mmHg, P < 0.001) and diastolic (78.14 ± 14.21 vs. 67.54 ± 11.46 mmHg, P < 0.001) blood pressures. The distribution curves of mean AOV differed between patients and controls (smaller AOV for larger vessels in patients; P < 0.001) as well as between patients without retinopathy and those with non-proliferative diabetic retinopathy (NPDR); with larger AOV for smaller vessels in NPDR (P < 0.001). Controlling for the effect of confounders, patients had a smaller total AOV, larger total SD of AOV, and a more skewed distribution curve of vessels compared to controls.Conclusion:Presence of diabetes mellitus is associated with contraction of larger vessels in the conjunctiva. Smaller vessels dilate with diabetic retinopathy. These findings may be useful in the photographic screening of diabetes mellitus and retinopathy.
The identification of the optic nerve head (ONH) is necessary preprocessing step in retinal image analysis, for automated extraction of the anatomical components in retinal images. In this study, a new image processing method based on Radon transform (RT) and multi-overlapping windows was proposed for ONH detection in fluorescein angiography (FA) fundus images. At first, RT was applied to all fundus sub images to find candidates for the location of the ONH. Then, the accurate location was found using the minimum mean square error estimation. The results of our automated method for the ONH detection in the images showed sensitivity and specificity of 90.54%, 98.51% respectively for pixel based analysis, and according to manual ONH detection, our automated algorithm found 89 ONH out of 100 in true location for FA images. This study addresses a novel method in detection of retinal land marks. Sensitivity and specificity of this algorithm seems to be acceptable in comparison with other detection methods.
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