Contrast-enhanced magnetic resonance liver image registration, segmentation, and feature analysis for liver disease diagnosis

Image segmentation;Image registration;Contrast-enhanced MRI;Feature analysis

Oh, Ji Hun ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Image segmentation;    Image registration;    Contrast-enhanced MRI;    Feature analysis;   

Others  :  https://smartech.gatech.edu/bitstream/1853/45912/1/oh_jihun_201212_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The global objectives of this research are to develop a liver-specific magnetic resonance (MR) image registration and segmentation algorithms and to find highly correlated MR imaging features that help automatically score the severity of chronic liver disease (CLD). For a concise analysis of liver disease, time sequences of 3-D MR images should be preprocessed through an image registration to compensate for the patient motion, respiration, or tissue motion. To register contrast-enhanced MR image volume sequences, we propose a novel version of the demons algorithm that is based on a bi-directional local correlation coefficient (Bi-LCC) scheme. This scheme improves the speed at which a convergent sequence approaches to the optimum state and achieves the higher accuracy. Furthermore, the simple and parallelizable hierarchy of the Bi-LCC demons can be implemented on a graphics processing unit (GPU) using OpenCL. To automate segmentation of the liver parenchyma regions, an edge function-scaled region-based active contour (ESRAC), which hybridizes gradient and regional statistical information, with approximate partitions of the liver was proposed. Next, a significant purpose in grading liver disease is to assess the level of remaining liver function and to estimate regional liver function. On motion-corrected and segmented liver parenchyma regions, for quantitative analysis of the hepatic extraction of liver-specific MRI contrast agent, liver signal intensity change is evaluated from hepatobiliary phases (3-20 minutes), and parenchymal texture features are deduced from the equilibrium (3 minutes) phase. To build a classifier using texture features, a set of training input and output values, which is estimated by experts as a score of malignancy, trains the supervised learning algorithm using a multivariate normal distribution model and a maximum a posterior (MAP) decision rule. We validate the classifier by assessing the prediction accuracy with a set of testing data.