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

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dc.contributor.advisor Hu, Xiaoping
dc.contributor.advisor Yezzi, Anthony
dc.contributor.author Oh, Ji Hun en_US
dc.contributor.committeeMember Butera, Robert
dc.contributor.committeeMember Martin, Diego
dc.contributor.committeeMember Vela, Patricio
dc.contributor.committeeMember Zhou, Guotong
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2013-01-17T21:59:32Z
dc.date.available 2013-01-17T21:59:32Z
dc.date.issued 2012-11-13 en_US
dc.description.abstract 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. en_US
dc.description.degree PhD en_US
dc.identifier.uri http://hdl.handle.net/1853/45912
dc.publisher Georgia Institute of Technology en_US
dc.subject Image segmentation en_US
dc.subject Image registration en_US
dc.subject Contrast-enhanced MRI en_US
dc.subject Feature analysis en_US
dc.subject.lcsh Liver Diseases
dc.subject.lcsh Liver Diseases Diagnosis
dc.subject.lcsh Magnetic resonance imaging
dc.subject.lcsh Contrast-enhanced magnetic resonance imaging
dc.subject.lcsh Image processing Digital techniques
dc.title Contrast-enhanced magnetic resonance liver image registration, segmentation, and feature analysis for liver disease diagnosis en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Yezzi, Anthony
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 53ee63a2-04fd-454f-b094-02a4601962d8
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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