Open AccessThree-dimensional kidney’s stones segmentation and chemical composition detection
Author(s) -
Hiam Alquran,
Mohammed Alslity,
Isam Abu Qasmieh,
Khaled Alawneh,
Ali Mohammad Alqudah,
Ahmed Al-Rasheed,
Mohammed AlHawari
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i5.pp3988-3995
Subject(s) - kidney stones , medicine , kidney , segmentation , radiology , kidney disease , renal pelvis , urinary system , ultrasound , computer science , artificial intelligence , surgery
Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. 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, and it 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. The result agreed with medical doctor's diagnosis. The system can be improved by analyzing the stones in the laboratory and 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%.