Kidney Stone Detection using Image Segmentation and Radial Transform

Kidney stones are a common urological issue that affects millions of people worldwide and, if left untreated, can cause significant discomfort and consequences. To avoid catastrophic results and enhance treatment regimens, it is critical to adopt early detection and exact diagnosis. New medical imaging methods have made it much easier to diagnose and treat kidney stones. Nonetheless, we have a long way to go before we can correctly and precisely identify these stones, especially in tough settings when the stones are small, hidden, or located in hard-to-see regions. This paper presents a unique method for identifying kidney stones that combines cutting-edge image segmentation algorithms with radial transform techniques. The suggested technique intends to overcome some of the shortcomings with current detection methods to improve the reliability and accuracy of stone recognition in medical imaging.