Image Processing for Kidney Stone Segmentation and Surface Area Estimation using CT Scans

Zuhier Humady Hussien, Rabab Saadoon Abdoon and Nihad Abdulameer Salih

References:

1. X. Long, M. Huang, and H. Hu, “Revascularization may accelerate renal dysfunction in hypertensive patients with moderate atherosclerotic stenosis of renal arteries,” Medical Hypotheses, vol. 144, p. 110008, 2020.
2. C. Türk et al., “EAU guidelines on interventional treatment for urolithiasis,” European Urology, vol. 69, no. 3, pp. 475-482, 2016.
3. B. E. Knudsen, “Laser fibers for holmium: YAG lithotripsy: What is important and what is new,” Challenge, vol. 3, p. 6, 2019.
4. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, Pearson Education India, 2004.
5. L. Yang et al., “Contrast enhancement boost improves the image quality of CT angiography derived from 80-kVp cerebral CT perfusion data,” BMC Medical Imaging, vol. 24, no. 1, 2024, , doi: 10.1186/s12880-024-01373-7.
6. A. Hore and D. Ziou, “Image quality metrics: PSNR vs. SSIM,” in 2010 20th International Conference on Pattern Recognition, IEEE, 2010, pp. 2366-2369.
7. J. Moreau et al., “Contrast quality control for segmentation task based on deep learning models—Application to stroke lesion in CT imaging,” Frontiers in Neurology, vol. 16, p. 1434334, 2025.
8. G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60-88, 2017.
9. A. Pimpalkar, D. K. J. B. Saini, N. Shelke, A. Balodi, G. Rapate, and M. Tolani, “Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging,” Scientific Reports, vol. 15, no. 1, p. 10741, 2025.
10. D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual Review of Biomedical Engineering, vol. 19, no. 1, pp. 221-248, 2017.
11. R. Szeliski, Computer Vision: Algorithms and Applications, Springer Nature, 2022.
12. D.-H. Jang et al., “Kidney, ureter, and urinary bladder segmentation based on non-contrast enhanced computed tomography images using modified U-Net,” Scientific Reports, vol. 14, no. 1, p. 15325, 2024.
13. D. L. Pham, C. Xu, and J. L. Prince, “Current methods in medical image segmentation,” Annual Review of Biomedical Engineering, vol. 2, no. 1, pp. 315-337, 2000.
14. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, 2004.
15. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-168, 2004.
16. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Computers & Geosciences, vol. 10, no. 2-3, pp. 191-203, 1984, , doi: 10.1016/0098-3004(84)90020-7.
17. F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV), IEEE, 2016, pp. 565-571.
18. R. C. Gonzalez, Digital Image Processing, Pearson Education India, 2009.
19. A. K. Jain, Fundamentals of Digital Image Processing, 1989.
20. N. C. Grossmann, F. Panthier, L. Afferi, P. Kallidonis, and B. K. Somani, “Measuring kidney stone volume – practical considerations and current evidence from the EAU endourology section,” Current Opinion in Urology, vol. 35, no. 4, 2025, , [Online]. Available: https://journals.lww.com/courology/fulltext/2025/07000/measuring_kidney_stone_volume___practical.4.aspx
21. R. P. Marcus et al., “Detection and characterization of renal stones by using photon-counting–based CT,” Radiology, vol. 289, no. 2, pp. 436-442, Aug. 2018, , doi: 10.1148/radiol.2018180126.
22. M. Lei, H. Wu, X. Lv, and X. Wang, “Condseg: A general medical image segmentation framework via contrast-driven feature enhancement,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2025, pp. 4571-4579.
23. Z. Zhang and U. Bagci, “Segmentation quality and volumetric accuracy in medical imaging,” arXiv: Computer Science, 2024, , [Online]. Available: https://arxiv.org/abs/2404.17742
24. Q. Huynh-Thu and M. Ghanbari, “Scope of validity of PSNR in image/video quality assessment,” Electronics Letters, vol. 44, no. 13, pp. 800-801, 2008.
25. L. Maier-Hein and B. Menze, “Metrics reloaded: Pitfalls and recommendations for image analysis validation,” arXiv.org, 2022.
26. A. Breger et al., “A study on the adequacy of common IQA measures for medical images,” in Proceedings of the 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024), Springer Nature Singapore, 2025, pp. 451-462.
27. L. Kong, M. Huang, L. Zhang, and L. W. C. Chan, “Enhancing diagnostic images to improve the performance of the Segment Anything Model in medical image segmentation,” Bioengineering, vol. 11, no. 3, 2024, , doi: 10.3390/bioengineering11030270.
28. Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? A new look at signal fidelity measures,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98-117, 2009, , doi: 10.1109/MSP.2008.930649.
29. Y. Xie et al., “Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT,” IEEE Transactions on Medical Imaging, vol. 38, no. 4, pp. 991-1004, 2018.