Medical image registration on tumor growth with time series

Wang, Kuocheng

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https://hdl.handle.net/2142/92660 Copy

Description

Title

Medical image registration on tumor growth with time series

Author(s)

Wang, Kuocheng

Issue Date

2016-07-21

Director of Research (if dissertation) or Advisor (if thesis)

Kesavadas, Thenkurussi

Committee Member(s)

LaValle, Steve

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Systems & Entrepreneurial Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(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.

Graduation Semester

2016-08

Type of Resource

text

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http://hdl.handle.net/2142/92660

Copyright and License Information

Copyright 2016 Kuocheng Wang

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