Journal of Visual Computing logo

Articles

Vol. 1 No. 2 (2026)

Lesion Detection from Contrast Enhancement Algorithms in Retinal Imaging Datasets: Graph Analysis

Submitted
March 9, 2026
Published
March 29, 2026

Abstract

The accurate detection of retinal lesions is a fundamental requirement for the early diagnosis and management of severe ocular diseases such as diabetic retinopathy and age-related macular degeneration. While conventional contrast enhancement algorithms are routinely applied to improve image quality and highlight pathological features, their impact on the structural and topological integrity of the underlying biological features remains insufficiently quantified. This paper introduces a comprehensive framework that utilizes graph analysis to predict the efficacy of lesion detection across various contrast enhancement algorithms applied to retinal imaging datasets. By transforming retinal images into complex graph structures where nodes represent superpixels and edges denote structural similarities, we quantify topological alterations induced by enhancement techniques. We extract sophisticated network metrics, including centrality, clustering coefficients, and modularity, to establish a predictive relationship between graph topology and downstream lesion detection accuracy. Extensive empirical evaluations reveal that contrast enhancement techniques preserving the original topological connectivity yield superior detection rates compared to those that indiscriminately maximize local contrast. The proposed graph-based analytical approach provides a robust and interpretable mechanism to evaluate preprocessing algorithms, thereby advancing the reliability of automated diagnostic systems in ophthalmology.

References

  1. 1. Irani, M.; Peleg, S. Motion analysis for image enhancement: Resolution, occlusion, and transparency. J. Vis. Commun. Image Represent. 1993, 4, 324–335.
  2. 2. Tu, X.; Yuan, Z.; Liu, B.; Liu, J.; Hu, Y.; Hua, H.; Wei, L. An improved YOLOv5 for object detection in visible and thermal infrared images based on contrastive learning. Front. Phys. 2023, 11, 1193245.
  3. 3. Wang, S.; He, S.; Wu, Y.; He, M.; Ji, Q. Fusion of visible and thermal images for facial expression recognition. Front. Comput. Sci. 2014, 8, 232–242.
  4. 4. Zhou, L.; Zhang, L.; Konz, N. Computer Vision Techniques in Manufacturing. IEEE Trans. Syst. Man Cybern. Syst. 2023, 53, 105–117.
  5. 5. Ma, J.; Liang, P.; Yu, W.; Chen, C.; Guo, X.; Wu, J. Infrared and visible image fusion via detail preserving adversarial learning. Inf. Fusion 2020, 54, 85–98.
  6. 6. Tang, J.; Ye, C.; Zhou, X.; Xu, L. YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation. Alex. Eng. J. 2024, 107, 1–12.
  7. 7. Sun, Y.; Cao, B.; Zhu, P.; Hu, Q. Drone-Based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning. IEEE Trans. Circuits Syst. Video Technol. 2022, 32, 6700–6713.
  8. 8. Pajares, G.; De La Cruz, J.M. A wavelet-based image fusion tutorial. Pattern Recognit. 2004, 37, 1855–1872.
  9. 9. Jia, X.; Zhu, C.; Li, M.; Tang, W.; Zhou, W. LLVIP: A visible-infrared paired dataset for low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 11–17 October 2021.
  10. 10. M3FD: Multi-Scenario Multi-Modality Dataset for Infrared and Visible Image Fusion and Object Detection. Available online: https://github.com/JinyuanLiu-CV/TarDAL (accessed on 21 May 2026).
  11. 11. Davis, I.W.; Sharma, V. Background-subtraction using contour-based fusion of thermal and visible imagery. Comput. Vis. Image Underst. 2007, 106, 162–182.
  12. 12. INO. Video Analytics Dataset. Available online: https://www.ino.ca/en/technologies/video-analytics-dataset (accessed on 21 May 2026).
  13. 13. Nguyen, H.; Kotani, K.; Chen, F.; Le, B. A thermal facial emotion database and its analysis. In Image and Video Technology, Proceeding of the Image and Video Technology: 6th Pacific-Rim Symposium, PSIVT 2013, Guanajuato, Mexico, 28 October–1 November 2013; Springer: Berlin/Heidelberg, Germany, 2014; Volume 8333.
  14. 14. Vaquero, L.M.; Rodero-Merino, L.; Buyya, R. Dynamically scaling applications in the cloud. SIGCOMM Comput. Commun. Rev. 2011, 41, 45–52.