Three-Dimensional Kidney’s Stones Segmentation and Chemical Composition Detection

The two kidneys in the body are bean-shaped organs in the renal system. They help the body to filter waste and extra fluid as urine. Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. They consist of crystalline organic components and they are formed when the urine becomes supersaturated with salt and minerals, where calcium oxalate is the main constituent in most stones. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. For patients with recurrent kidney stones disease, CT is a critical tool for stones’ size quantification, beside to determining their location, as well as for the assessment of metabolic activity of nephrolithiasis. In this paper, we design an automated system for 3D kidney segmentation and stones detection in addition to their number and size evaluation. The proposed system is built based on CT kidney image series of 10 subjects, four healthy subjects (with no stones) and the rest have stones based on medical doctor diagnosis, and its performance is tested based on 32 CT kidney series images. The designed system shows its ability to extract kidney either in abdominal or pelvis non-contrast series CT images. The proposed system distinguishes the stones from the surrounding tissues in the kidney image, besides to its ability to analyze the stones and classify them in vivo for further medical treatment, where the study analysis agrees with the medical doctor's diagnosis. The system can be improved by analyzing the stones in the laboratory and by using a large CT dataset. The present method is not limited to extract stones but, also a new approach is proposed to extract the 3D kidneys as well with accuracy 99%.