Kidney Stone Detection

A Matlab app for kindey stone detection

Kidney stones are a silent torment — a condition that often creeps in unnoticed, only making its presence known through sudden, excruciating pain. Nephrolithiasis, the medical term for kidney stones, affects millions of lives worldwide, bringing discomfort that ranges from mild irritation to severe agony. Left undiagnosed and untreated, these stones can block the urinary tract, cause infections, and even lead to renal failure[1].

Early detection is important, but the challenges of identifying stones in ultrasound images make this far from simple. The low contrast and inherent speckle noise in ultrasound scans often obscure critical details, making manual detection time-consuming and prone to human error [2]. Traditional diagnostic methods rely heavily on the expertise of radiologists, yet even the most trained eyes can miss tiny stones, leading to delayed treatment and unnecessary suffering.

Our project will address this issue by developing an automated kidney stone detection system using MATLAB-based image processing techniques. Through a series of carefully designed steps — including grayscale conversion, adaptive thresholding, morphological operations, and noise reduction — we will have enhanced image quality and accurately isolate the suspected stone regions. By refining regions of interest and applying dynamic filtering, our system shows a precise and efficient method for stone detection, reducing the workload on medical professionals and increasing diagnostic reliability.

This project is more than just an exercise in coding and algorithms — it’s a step toward easing human pain. It’s about using technology to bring faster diagnoses, better treatment planning, and ultimately, a better quality of life for patients.