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Title:Medical image registration on tumor growth with time series
Author(s):Wang, Kuocheng
Advisor(s):Kesavadas, Thenkurussi
Contributor(s):LaValle, Steve
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Systems & Entrepreneurial Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Tumor registration
Abstract:Data registration is a common process in medical image analysis. The goal of data registration is to solve the transformation problem with multiple images' alignment. Conventionally, diagnosing the tumors periodically requires understanding the growth and spread of tumor which is performed by doctors by visual inspections of multiple MRI scan taken over different stages in time series. Due to the misalignment of patient's posture, comparison of these multiple MRI scans is tedious. This problem is addressed often using image registration of non-rigid body. In this method the features are first extracted from the original data set. There are several features one can extract like chamfer, line, region, etc. The feature we chose for the first method was the best fit plane, the second method was the principal axis. Those results are later compared with Iterative Closest Point(ICP) method. The 2 main motivations are 1. to compare different image data set to correlate different measures of anatomical structures, 2. to aid doctors measure the change in dynamic structural patterns of tumor growth, brain development, etc. In this thesis, we present data registration using rigid body for tumor growth which was usually explore with non-rigid body registration. Furthermore, we demonstrate different methods of image registration on rigid body for a time series of tumors. The results were qualitatively compared based on template matching of time series tumor data.
Issue Date:2016-07-21
Rights Information:Copyright 2016 Kuocheng Wang
Date Available in IDEALS:2016-11-10
Date Deposited:2016-08

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