A set of tubes known as bile ducts connects the liver to an organ below it directly that is called Gallbladder. The dilation of a bile duct is an important indicator regarding any serious issue in the human body. Number of reasons may cause bile duct dilation, such as: stones, tumors which commonly occur due to pancreas or papilla of vater. In this paper, the main contributions are: 1) a novel framework that consists of three phases to be applied on a set of Magnetic Resonance Imaging (MRI) images 2) an extracted set of features with their accurate values that express the condition of the biliary trees from the MRI images. Such dataset can be used in several applications to determine whether a bile duct is dilated or not. The dataset is organized as the following: half of the MRI images are for normal bile ducts, while the other half is for dilated bile ducts. To extract the useful features to diagnose the medical condition of the bile ducts from the MRI images, we implemented and applied the proposed framework that is started by using the enhanced active contour technique without edges in combination with Denoising Convolutional Neural Networks (DnCNN) to perform the segmentation and features extraction process. After that, the output of the segmentation process is the segmented biliary tree that will be used later to extract the needful features to make a diagnostic decision whether there is a dilation or not by comparing the features values of the normal versus the dilated bile ducts. We applied the feed forward neural network with backpropagation training algorithm for classification purposes. According to the experiments, the overall accuracy of the proposed framework was 90.00%. Such approach improves and increases the accuracy of the physicians’ diagnostic decisions which is considered as of significant importance for treatment and cure.