Artificially Intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104263 - 104263

Published: Feb. 4, 2025

Language: Английский

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A fast and fully automated system for segmenting retinal blood vessels in fundus images

Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 34(1)

Published: Jan. 1, 2025

Abstract The problem of segmenting retinal blood vessels in fundus images arises from the challenges accurately detecting and delineating due to their complex structures, varying sizes, overlapping features. Manual segmentation is time-consuming prone human error, leading inconsistent results. Additionally, existing automated methods often struggle with low-quality or variations illumination, hindering effectiveness. Therefore, there a pressing need for an efficient accurate system improve outcomes better diagnosis diseases. This study proposes fully model vessel images, addressing key such as poor image quality, weak detection, inhomogeneity contrast. Macular degeneration diabetic retinopathy are major causes vision impairment, making analysis crucial. proposed enhances quality through novel pre-processing pipeline that includes logarithmic contrast enhancement, noise reduction using improved wavelet transform shrinkage, anisotropic diffusion filtering edge enhancement. method combines morphological operations optimized Canny detector, effectively identifying vessels. approach aims accuracy efficiency analysis, overcoming limitations manual vascular structures. results obtained DRIVE dataset achieved high values (Acc, 99%), sensitivity (Sen, 95.83%), specificity (Spe, 98.62%), positive predictive value (PPV, 91.34%), negative (NPV, 94%). In addition, high-resolution were equally satisfactory, achieving Acc., Sen., Spe., PPV, NPV 99.11, 97.97, 98.97, 97.98, 100%, respectively. These outperform gold standard state-of-the-art schemes date. increases performance reliability process detection images.

Language: Английский

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A comprehensive review of optic disc segmentation methods in adult and pediatric retinal images: from conventional methods to artificial intelligence (CR-ODSeg-AP-CM2AI)

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Feb. 3, 2025

Language: Английский

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Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component

Measurement, Journal Year: 2024, Volume and Issue: 242, P. 116229 - 116229

Published: Nov. 14, 2024

Language: Английский

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