Mathematical Model of the Deterministic Components of Artifacts in Fundus Photography

TY - GEN

T1 - Mathematical Model of the Deterministic Components of Artifacts in Fundus Photography

AU - Menolotto, Matteo

AU - Giardini, Mario Ettore

N1 - Conference code: 47

PY - 2025/12/3

Y1 - 2025/12/3

N2 - 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.

AB - 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.

KW - artifacts

KW - fundoscopy

KW - modelling

U2 - 10.1109/EMBC58623.2025.11253127

DO - 10.1109/EMBC58623.2025.11253127

M3 - Conference contribution

C2 - 41337419

SN - 9798331586195

T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

BT - 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

PB - IEEE

T2 - 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 14 June 2025 through 17 June 2025

ER -