TY - GEN
T1 - Mathematical Model of the Deterministic Components of Artifacts in Fundus Photography
AU - Menolotto, Matteo
AU - Giardini, Mario Ettore
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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 - 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.
UR - https://www.scopus.com/pages/publications/105023715818
U2 - 10.1109/EMBC58623.2025.11253127
DO - 10.1109/EMBC58623.2025.11253127
M3 - Conference proceeding
C2 - 41337419
AN - SCOPUS:105023715818
T3 - Proceedings of the 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 2025 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025
Y2 - 14 July 2025 through 18 July 2025
ER -