Skip to main navigation menu Skip to main content Skip to site footer

IMAGE PREPROCESSING AND ENHANCEMENT FOR ENDOSCOPIC IMAGE ANALYSIS IN GASTROINTESTINAL DISEASE DETECTION

PDF

Abstract

Endoscopic imaging plays a central role in the diagnosis of gastrointestinal diseases. It allows doctors to directly observe the inner surface of the esophagus, stomach, duodenum, colon, and rectum. Through endoscopy, clinicians can detect inflammation, ulcers, erosions, polyps, bleeding areas, and early signs of cancer. In many cases, endoscopy also allows biopsy and minimally invasive treatment.


References

  1. Z. An et al., “EIEN: Endoscopic Image Enhancement Network Based on Retinex Theory,” Sensors, vol. 22, no. 14, Art. no. 5464, 2022.
  2. E. Mou, H. Wang, X. Chen, Z. Li, E. Cao, Y. Chen, Z. Huang, and Y. Pang, “Retinex theory-based nonlinear luminance enhancement and denoising for low-light endoscopic images,” BMC Medical Imaging, vol. 24, Art. no. 207, 2024.
  3. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Computer Vision, Graphics, and Image Processing, vol. 39, no. 3, pp. 355–368, 1987.
  4. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. IEEE International Conference on Computer Vision, 1998, pp. 839–846.
  5. A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 60–65.
  6. K. Ramalingam, “Enhancing Colonoscopy Image Quality Through Multi-Step Computational Pre-Processing Techniques,” Informatica, vol. 48, pp. 47–60, 2024.
  7. Z. Nie, M. Xu, Z. Wang, X. Lu, and W. Song, “A Review of Application of Deep Learning in Endoscopic Image Processing,” Journal of Imaging, vol. 10, no. 11, Art. no. 275, 2024.
  8. H. D. Viet et al., “Validation of YOLOv8 algorithm in detecting colon polyps in colonoscopy images,” Journal of Medical Artificial Intelligence, 2025.
  9. A. Krenzer et al., “A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Learning,” 2023.
  10. O. Zare et al., “LightAttn-YOLO-V8: An Efficient Colorectal Polyp Detection Method,” Scientific Reports, 2026.

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.