Mathematical Model of the Deterministic Components of Artifacts in Fundus Photography.

Matteo Menolotto, Mario Ettore Giardini
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引用次数: 0

Abstract

This work introduces a novel phenomenological model designed to replicate the deterministic aspects of artifacts that affect retinal imaging. To validate the model's ability to reproduce real artifacts, we utilized the CORD database, which contains retinal images affected by artifacts, along with corresponding clinically standard-quality images, serving as ground truth. The model was implemented in a Matlab script recreating various artifact distortions. The results demonstrate a robust correlation between the quality attributes of simulated artifact-affected images and real-world artifacts, with ANOVA tests yielding p-values > 0.05 across the most discriminative features (e.g., mean, IQR, and BVC). Furthermore, a quality classification analysis using Neighbourhood Components Analysis showed overlapping distributions between real and generated artifacts, supporting the model's ability to mimic realistic quality deterioration. This underscores the model's utility as a tool for generating synthetic artifacts, addressing the current lack of available datasets, with potential impact on the development of quality retrieval algorithms and modeling in digital retinal images.Clinical Relevance-The proposed model enables the generation of synthetic retinal imaging artifacts that closely resemble real-world distortions, providing a valuable tool for developing and evaluating quality enhancement algorithms in clinical ophthalmic imaging.

眼底摄影中人工制品确定性成分的数学模型。
这项工作介绍了一种新的现象学模型,旨在复制影响视网膜成像的人工制品的确定性方面。为了验证模型重现真实伪影的能力,我们使用了CORD数据库,其中包含受伪影影响的视网膜图像,以及相应的临床标准质量图像,作为基础事实。该模型在Matlab脚本中实现,重新创建各种工件扭曲。结果表明,模拟伪影影响图像的质量属性与现实世界伪影之间存在强大的相关性,方差分析测试在最具判别性的特征(例如,平均值、IQR和BVC)上产生的p值为> 0.05。此外,使用邻域成分分析的质量分类分析显示了真实和生成的工件之间的重叠分布,支持模型模拟现实质量恶化的能力。这强调了该模型作为生成合成工件的工具的实用性,解决了当前缺乏可用数据集的问题,并对数字视网膜图像中高质量检索算法和建模的发展产生了潜在影响。临床相关性-该模型能够生成与现实世界畸变非常相似的合成视网膜成像伪影,为开发和评估临床眼科成像质量增强算法提供了有价值的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
0.80
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0.00%
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