title: Improved biliary detection and diagnosis through intelligent machine analysis.
abstract: This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.
authors.affiliation: Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia. loges@ieee.org
authors.forename: Rajasvaran
authors.initials: R
authors.lastname: Logeswaran
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001649:Bile Duct Diseases / Q000175:diagnosis / Q000628:therapy; D001659:Biliary Tract / Q000473:pathology*; D049448:Cholangiopancreatography, Magnetic Resonance / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results