Abstract:

The main objective of this project is to detect the kidney stone from the digital ultrasound image of the kidney by performing various image processing techniques. Due to the varied texture and existence of speckle noise, detecting regions of interest in ultrasound pictures is a difficult process. Ultrasound scanning is the most common method of examining a patient for the presence of kidney stones. We created an application that aids the medical practitioner in selecting the region to be evaluated for the presence of stone using the suggested technology. The feature extraction is done on cropped portions that may have stones in them. The KNN classifier is used to categories images based on training data.

Introduction:

The production of crystals in the urine induced by genetic predisposition distinguishes renal calculus, also known as kidney stone formation. Even though many people, including children, are impacted by kidney stones, the vast majority of cases go unnoticed unless there is severe abdominal pain or an irregular urine color. Furthermore, persons with kidney stones exhibit common symptoms such as fever, discomfort, and nausea, which might be mistaken for other illnesses. Kidney stone identification is critical, especially early on, in order to receive adequate medical treatment. The presence of stones in the kidney reduces renal functionality and can potentially cause dilatation.
People who have never been diagnosed with this ailment will be affected by the severity of chronic kidney disease (CKD) / chronic renal failure (CRF). Because of its asymptomatic character, it is frequently detected during medical examinations for other diseases such as cardiovascular disease (CVD), diabetes, and other medical problems that predispose to the urogenital apparatus. Days, computer-assisted tools like ultrasound imaging, computed tomography (CT), and X rays gives the most accurate diagnostic tools for kidney stone screening and diagnosis.The main objective of this project is to detect the kidney stone from a digital ultrasound image of the kidney by performing various image processing techniques. But the image produced by the ultrasound techniques is not suitable for further processing due to low contrast and the presence of speckle noise.Hence, the study also examined the effectiveness of various diagnosis techniques on the ultrasound image to enhance the quality of the image. Further, enhanced ultrasound image will be used to locate the exact position of the stone. The main motive of this project was to develop an elementary and straightforward technique to find the stone in the kidney. This detection can be done in any available PC’s and hence any normal being can check an ultrasound for a kidney stone and dissolve it in the stone

Problem statement:

Current methods for kidney stone detection in medical imaging lack precision and reliability, leading to potential misdiagnosis or oversight of small or subtle stones. The manual analysis of kidney stone images is labor-intensive and time-consuming for healthcare professionals, delaying diagnosis and treatment initiation. Ensuring that the developed technology is accessible to healthcare facilities of varying resources and that its implementation does not significantly increase the cost of diagnosis and treatment.

Objective:

Develop algorithms or image processing techniques that can accurately detect the presence of kidney stones in medical imaging such as ultrasounds, CT scans, or X-rays. Early detection aids in timely intervention and treatment.