  The biliary tree is a network of tubes that connects the liver to the
gallbladder, an organ right beneath it. The bile duct is the major tube in the
biliary tree. The dilatation of a bile duct is a key indicator for more major
problems in the human body, such as stones and tumors, which are frequently
caused by the pancreas or the papilla of vater. The detection of bile duct
dilatation can be challenging for beginner or untrained medical personnel in
many circumstances. Even professionals are unable to detect bile duct
dilatation with the naked eye. This research presents a unique vision-based
model for biliary tree initial diagnosis. To segment the biliary tree from the
Magnetic Resonance Image, the framework used different image processing
approaches (MRI). After the image's region of interest was segmented, numerous
calculations were performed on it to extract 10 features, including major and
minor axes, bile duct area, biliary tree area, compactness, and some textural
features (contrast, mean, variance and correlation). This study used a database
of images from King Hussein Medical Center in Amman, Jordan, which included 200
MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After
the characteristics are extracted, various classifiers are used to determine
the patients' condition in terms of their health (normal or dilated). The
findings demonstrate that the extracted features perform well with all
classifiers in terms of accuracy and area under the curve. This study is unique
in that it uses an automated approach to segment the biliary tree from MRI
images, as well as scientifically correlating retrieved features with biliary
tree status that has never been done before in the literature.
