Tumors are generally difficult to detect in Magnetic Resonance (MR) images as they can be of varying intensities and do not appear as clear structures on these images. This difficulty is more prominent in MR Cholangiopancreatography (MRCP), which is the MR technology using a special sequence of T2-weighted imaging to identify the biliary tract, pancreatic duct, and gallbladder in the liver region, as MRCP images are more noisy in nature and are acquired for a more focused area with too much flexibility in position orientation for convenient computer-aided diagnosis. Based on the principle that the tumor mass manifests itself as blockage of the biliary tree structure, this paper introduces a technique that uses a region growing algorithm to identify discontinuities in the biliary tree as a means to preliminary detection of a possible tumor, in a fashion similar to the visual observation used by most radiologists in making their preliminary diagnosis. Through the use of appropriate image normalization, watershed segmentation, thresholding, rule-based region growing, and region analysis, the proposed technique is shown in this paper to be successful in identifying MRCP images with liver carcinoma from those with normal liver. Acquisition standardization, interactive image selection, and optimum image orientation will further enhance the accuracy of this proposed scheme for use in aiding clinical diagnosis at medical institutions.
Matched MeSH terms: Cholangiopancreatography, Magnetic Resonance/methods*
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.
Matched MeSH terms: Cholangiopancreatography, Magnetic Resonance/methods*
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.
Matched MeSH terms: Cholangiopancreatography, Magnetic Resonance/methods*