Kidney Stone Detection Using Image Processing

Kidney stone disease (nephrolithiasis) is one of the most common and painful urological disorders affecting millions of individuals worldwide. Early and accurate detection of kidney stones is essential to prevent complications such as urinary obstruction, infection, or renal failure. Traditional diagnostic methods rely heavily on manual interpretation of Computed Tomography (CT) scan images by radiologists, which is time-consuming and prone to human error. This research proposes an automated kidney stone detection system using image processing techniques applied to CT scan images. The system performs preprocessing steps such as noise reduction and contrast enhancement, followed by segmentation to isolate the kidney region. Techniques like thresholding, edge detection, and morphological operations are employed to identify potential stone regions. Machine learning algorithms are then used to classify and validate the presence and size of the stones. The experimental results show that the proposed method achieves high accuracy and reliability compared to traditional manual analysis. This automated image-processing-based detection system significantly reduces diagnostic time, minimizes human error, and supports radiologists in making faster and more precise clinical decisions.