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The biliary tree is a network of tubes that connects the liver to thegallbladder, an organ right beneath it. The bile duct is the major tube in thebiliary tree. The dilatation of a bile duct is a key indicator for more majorproblems in the human body, such as stones and tumors, which are frequentlycaused by the pancreas or the papilla of vater. The detection of bile ductdilatation can be challenging for beginner or untrained medical personnel inmany circumstances. Even professionals are unable to detect bile ductdilatation with the naked eye. This research presents a unique vision-basedmodel for biliary tree initial diagnosis. To segment the biliary tree from theMagnetic Resonance Image, the framework used different image processingapproaches (MRI). After the image's region of interest was segmented, numerouscalculations were performed on it to extract 10 features, including major andminor axes, bile duct area, biliary tree area, compactness, and some texturalfeatures (contrast, mean, variance and correlation). This study used a databaseof images from King Hussein Medical Center in Amman, Jordan, which included 200MRI images, 100 normal cases, and 100 patients with dilated bile ducts. Afterthe characteristics are extracted, various classifiers are used to determinethe patients' condition in terms of their health (normal or dilated). Thefindings demonstrate that the extracted features perform well with allclassifiers in terms of accuracy and area under the curve. This study is uniquein that it uses an automated approach to segment the biliary tree from MRIimages, as well as scientifically correlating retrieved features with biliarytree status that has never been done before in the literature.

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision