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Automatic rigid and deformable medic...

Yu, Hongliang.

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Automatic rigid and deformable medical image registration.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automatic rigid and deformable medical image registration./
Author:	Yu, Hongliang.
Description:	116 p.
Notes:	Adviser: John M. Sullivan, Jr.
Contained By:	Dissertation Abstracts International66-04B.
Subject:	Engineering, Mechanical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3172527
ISBN:	9780542094798

Automatic rigid and deformable medical image registration.
Yu, Hongliang.

Automatic rigid and deformable medical image registration. - 116 p.

Adviser: John M. Sullivan, Jr.

Thesis (Ph.D.)--Worcester Polytechnic Institute, 2005.

Advanced imaging techniques have been widely used to study the anatomical structure and functional metabolism in medical and clinical applications. Images are acquired from a variety of scanners (CT/MR/PET/SPECT/Ultrasound), which provide physicians with complementary information to diagnose and detect specific regions of a patient. However, due to the different modalities and imaging orientations, these images rarely align spatially. They need to be registered for consistent and repeatable analyses. Therefore, image registration is a critical component of medical imaging applications.

ISBN: 9780542094798Subjects--Topical Terms:

783786
Engineering, Mechanical.

Automatic rigid and deformable medical image registration.
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008 110523s2005 ||||||||||||||||| ||eng d
020 $a 9780542094798
035 $a (UMI)AAI3172527
035 $a AAI3172527
040 $a UMI $c UMI
100 1 $a Yu, Hongliang. $3 1269173
245 1 0 $a Automatic rigid and deformable medical image registration.
300 $a 116 p.
500 $a Adviser: John M. Sullivan, Jr.
500 $a Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 2281.
502 $a Thesis (Ph.D.)--Worcester Polytechnic Institute, 2005.
520 $a Advanced imaging techniques have been widely used to study the anatomical structure and functional metabolism in medical and clinical applications. Images are acquired from a variety of scanners (CT/MR/PET/SPECT/Ultrasound), which provide physicians with complementary information to diagnose and detect specific regions of a patient. However, due to the different modalities and imaging orientations, these images rarely align spatially. They need to be registered for consistent and repeatable analyses. Therefore, image registration is a critical component of medical imaging applications.
520 $a Since the brains of rodent animal mostly behave in the rigid manner, their alignments may be generally described by a rigid model without local deformation. Mutual information is an excellent strategy to measure the statistical dependence of image from mono-modality or multi-modalities. The registration system with rigid model was developed to combine with mutual information for functional magnetic resonance (fMRI) analysis, which has five components: (1) rigid body and affine transformation, (2) mutual information as the similarity measure, (3) partial volume interpolation, (4) multi-dimensional optimization techniques, and (5) multi-resolution acceleration.
520 $a In this research three innovative registration systems were designed with the configurations of the mutual information and optimization technique: (1) mutual information combined with the downhill simplex method of optimization, (2) the derivative of mutual information combined with Quasi-Newton method, (3) mutual information combined with hybrid genetic algorithm (large-space random search) to avoid local maximum during the optimization. These automatic registration systems were evaluated with a variety of images, dimensions and voxel resolutions. Experiments demonstrate that registration system combined with mutual information and hybrid genetic algorithm can provide robust and accurate alignments to obtain a composite activation map for functional MRI analysis.
520 $a In addition, deformable models (elastic and viscous fluid) were applied to describe the physical behavior of the soft tissues (female breast cancer images). These registration methods model the movement of image as an elastic or viscous fluid object with material attributes corresponding to the constitution of specific tissues. In these two models the physical behavior of deformable object is governed by Navier linear elastic equation or Navier-Stokes equation. The gradient of image intensity was selected as the driving force for the registration process. The equations were solved using finite difference approach with successive over-relaxation (SOR) solver. Soft tissue and synthetic images were used to verify the registration method. All of these advancements enhanced and facilitated the research on functional MR images for rodent animals and female breast cancer detection.
590 $a School code: 0774.
650 4 $a Engineering, Mechanical. $3 783786
650 4 $a Health Sciences, Radiology. $3 1019076
690 $a 0548
690 $a 0574
710 2 $a Worcester Polytechnic Institute. $3 1025154
773 0 $t Dissertation Abstracts International $g 66-04B.
790 $a 0774
790 1 0 $a Sullivan, John M., Jr., $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3172527