Proceedings of International Conference on Applied Innovation in IT  ·  2026/03/31  ·  Vol. 14  ·  Issue 1  ·  pp. 69–80
Image Processing for Kidney Stone Segmentation and Surface Area Estimation using CT Scans
Zuhier Humady Hussien, Rabab Saadoon Abdoon and Nihad Abdulameer Salih
📄 Download PDF DOI: 10.25673/123519
Abstract
This study presents an integrated image-processing framework for kidney stone segmentation and surface area estimation from helical CT scans to support ESWL treatment planning. The proposed approach combines pre-processing (Prewitt, Laplacian, and sharpening filters), segmentation methods (K-means clustering and Fuzzy C-Means), and contrast enhancement to improve stone visibility in noisy, low-contrast CT images. Image quality was evaluated using MSE, PSNR, and SSIM metrics, while segmentation accuracy was validated by comparing automatically extracted stone areas with expert urologist annotations. Experiments were conducted on 10 clinically selected CT cases (5 fragmentable and 5 non-fragmentable stones). Results show that Prewitt and sharpening filters provide improved edge preservation and structural similarity, while K-means (k = 4, 5) and FCM effectively segment stone regions under varying contrast conditions. The proposed framework achieved strong agreement with expert measurements, with most surface area deviations below 5%. Overall, the results demonstrate that combining classical filtering, clustering, and contrast enhancement significantly improves kidney stone localization and quantification. The method is clinically reliable for ESWL planning, though performance decreases for very small stones (<20 pixels), indicating a need for further refinement in low-resolution cases.
Keywords
Kidney Stone Fragmentation Helical Computed Tomography(CT) Pre-Image Filtering Image Segmentation K-Means FCM Contrast Adjustment ESWL MATLAB Image Enhancement.
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