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...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2006-08, Vol.44 (8), p.711-719
Main Author: Logeswaran, Rajasvaran
Format: Article
Language:English
Subjects:
Algorithms
Biliary Tract - diagnostic imaging
Cholangiopancreatography, Magnetic Resonance - methods
Choledocholithiasis - diagnostic imaging
Cholelithiasis - diagnostic imaging
Gallstones - diagnostic imaging
Humans
Neural networks
Neural Networks (Computer)
Radiographic Image Enhancement - methods
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Summary: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.
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-006-0083-8