Neural networks aided stone detection in thick slab MRCP images.
This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.
Logeswaran
Rajasvaran
R
Centre for Image Processing and Telemedicine, Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia. loges@mmu.edu.my
D000465:Algorithms; D001659:Biliary Tract / Q000000981:diagnostic imaging; D049448:Cholangiopancreatography, Magnetic Resonance / Q000379:methods*; D042883:Choledocholithiasis / Q000000981:diagnostic imaging; D002769:Cholelithiasis / Q000000981:diagnostic imaging*; D042882:Gallstones / Q000000981:diagnostic imaging; D006801:Humans; D016571:Neural Networks, Computer; D011856:Radiographic Image Enhancement / Q000379:methods