NDLI: Neural networks aided stone detection in thick slab MRCP images

Content Provider	Springer Nature Link
Author	Logeswaran, Rajasvaran
Copyright Year	2006
Abstract	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.
Starting Page	711
Ending Page	719
Page Count	9
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	44
Issue Number	8
e-ISSN	17410444
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-07-25
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computer-aided diagnosis Biliary stones detection Magnetic resonance Cholangiopancreatography Neural networks Disease detection Biomedical Engineering Computer Applications Imaging Radiology Neurosciences Human Physiology
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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