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*
A 5-year-old Chinese girl with 1-year history of progressive jaundice, steatorrhoea and pruritus was referred. Physical examination showed failure to thrive, marked jaundice, finger clubbing and hepatomegaly. There was laboratory evidence of cholestatic jaundice and autoimmunity, with marked elevation of alkaline phosphatase (ALP) and gamma-glutamyl transferase (gammaGT). Histology of percutaneous liver biopsy revealed hepatitis around the portal triad, as well as features of liver cirrhosis. Primary sclerosing cholangitis (PSC) overlapping with autoimmune hepatitis (AIH) was suspected. Endoscopic retrograde cholangiopancreatography (ERCP) was not feasible as there was no weight-appropriate ERCP scope available. Magnetic resonance cholangiopancreatography (MRCP) was performed and revealed areas of irregularity and slight attenuation of the right and left hepatic ducts, representing stricturing, in keeping with PSC. PSC/AIH overlap syndrome was diagnosed in this child in which MRCP has contributed to its diagnosis.
Matched MeSH terms: Cholangiopancreatography, Magnetic Resonance*
Automated computer analysis of magnetic resonance cholangiopancreatography (MRCP) (a focused magnetic resonance imaging sequence for the pancreatobiliary region of the abdomen) images for biliary diseases is a difficult problem because of the large inter- and intrapatient variations in the images, varying acquisition settings, and characteristics of the images, defeating most attempts to produce computer-aided diagnosis systems. This paper proposes a system capable of automated preliminary diagnosis of several diseases affecting the bile ducts in the liver, namely, dilation, stones, tumor, and cyst. The system first identifies the biliary ductal structure present in the MRCP images, and then proceeds to determine the presence or absence of the diseases. Tested on a database of 593 clinical images, the system, which uses visual-based features, has shown to be successful in delivering good performance of 70-90% even in the presence of multiple diseases, and may be useful in aiding medical practitioners in routine MRCP examinations.
Matched MeSH terms: Cholangiopancreatography, Magnetic Resonance*
Stones in the biliary tract are routinely identified using MRCP (magnetic resonance cholangiopancreatography). The noisy nature of the images, as well as varying intensity, size and location of the stones, defeat most automatic detection algorithms, making computer-aided diagnosis difficult. This paper proposes a multi-stage segment-based scheme for semi-automated detection of choledocholithiasis and cholelithiasis in the MRCP images, producing good performance in tests, differentiating them from "normal" MRCP images. With the high success rate of over 90%, refinement of the scheme could be applicable in the clinical environment as a tool in aiding diagnosis, with possible applications in telemedicine.
Matched MeSH terms: Cholangiopancreatography, Magnetic Resonance/statistics & numerical data*
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*
Primary sclerosing cholangitis (PSC) is the most common liver disease and known hepatobiliary complication of ulcerative colitis (UC). Concomitant PSC in UC is associated with increased risk of rapid progression of primary sclerosing cholangitis, and malignancy including colon carcinoma as well as hepatobiliary carcinoma. We report a case of a 26-year-old woman who was diagnosed as ulcerative colitis during her second pregnancy. Her liver function test showed a significant elevation of alkaline phosphatase (ALP) and gamma glutamyl transferase (GGT) with other parameters being within normal range. A clinical suspicion of primary sclerosing cholangitis was then made. Magnetic resonance cholangiopancreticography (MRCP) revealed beaded appearance of the right and left intrahepatic ducts with focal narrowing seen at the ducts, suggestive of primary sclerosing cholangitis. She was subsequently started on oral Ursodeoxycholic acid (UDCA) with improvement in her liver function test within 3 weeks of initiation of treatment.
Matched MeSH terms: Cholangiopancreatography, Magnetic Resonance