The method of vascular segmentation is considered as one of the main approaches to the creation of automated retinal analysis tools. Improved retinal image analysis that can be used for segmented vascular tree to calculate vessel diameter and tortuosity, differentiation of veins and arteries together with measurement of arteriovenous ratio. The algorithm of segmentation of the retinal vessels based on fuzzy clustering of c-means and the method of setting the level is proposed. Morphological processes, CLAHE, and appropriate image filtering techniques were used to enhance the picture before fuzzy clustering of vascular pixels. A method of segmentation on publicly available datasets that uses common validation metrics in retinal vessel segmentation is proposed.
The current state and prospects of development of methods of obtaining and analyzing images of the retina in important eye diseases and systemic diseases have been considered and analyzed. A method of fractal analysis of the retinal image based on the k-mean algorithm for the task of computer diagnostics and automated screening of hypertensive retinopathy has been developed and tested. The coefficients of wavelet decomposition of images of pathological conditions of the retina in angiopathy, macular degeneration, retinal degeneration and hypertensive retinopathy were calculated. An algorithm for finding the uncertainty of the quantitative values of the maximum modules of the wavelet decomposition coefficients in the presence of distorting obstacles is proposed.
The method of vascular segmentation is considered as one of the main approaches to the creation of automated retinal analysis tools. Improved retinal image analysis that can be used for segmented vascular tree to calculate vessel diameter and tortuosity, differentiation of veins and arteries together with measurement of arteriovenous ratio. The algorithm of segmentation of the retinal vessels based on fuzzy clustering of c-means and the method of setting the level is proposed. Morphological processes, CLAHE, and appropriate image filtering techniques were used to enhance the picture before fuzzy clustering of vascular pixels. A method of segmentation on publicly available datasets that uses common validation metrics in retinal vessel segmentation is proposed.
The method of vascular segmentation is considered as one of the main approaches to the creation of automated retinal analysis tools. Improved retinal image analysis that can be used for segmented vascular tree to calculate vessel diameter and tortuosity, differentiation of veins and arteries together with measurement of arteriovenous ratio. The algorithm of segmentation of the retinal vessels based on fuzzy clustering of c-means and the method of setting the level is proposed. Morphological processes, CLAHE, and appropriate image filtering techniques were used to enhance the picture before fuzzy clustering of vascular pixels. A method of segmentation on publicly available datasets that uses common validation metrics in retinal vessel segmentation is proposed.
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