In the brain mapping case, the geodesic closest points are used as the anatomical constraints for the inter-subject non-rigid registration. The method uses the anatomical constraint in the non-rigid registration which is much more reasonable for the anatomical correspondence. The registration result shows significant improvement comparing with the iterative closest points based method. / In the third application, we use the non-rigid registration method to register the different sweeps of freehand ultrasound images. We setup a 3D freehand ultrasound imaging system to capture images of a human anatomy such as liver, prostate, brain tumor and fetus. The arbitrary scanned image slices are reconstructed and resliced into volumetric dataset. We use a B-spline based non-rigid registration method to compounding different freehand ultrasound sweeps. This technique can be used to make 3D ultrasound models of fetus and other organs. / Medical image registration is an active research area during the last two decades. The registration technique can be widely used in the applications of the computer aided surgery, brain mapping and pathological detection and analysis. With the development of the computing power, fast and accurate registration techniques have been developed into necessary tools for quantitative analysis of the medical image. / Non-rigid registration methods can be used in atlas based image segmentation, inter-subject brain image registration and 3D freehand ultrasound modeling. In one of our proposed novel segmentation methods, we interleave the segmentation and the registration processes by using the segmentation to provide the anatomical constraints for registration to improve the atlas based non-rigid registration. This updated registration can be used to improve the new segmentation. This process is repeated until a good result in segmentation is obtained. / The registration methods can be classified into rigid and non-rigid registrations according to whether the anatomy is locally deformed or not. According to the sensor by which the images are taken, the registration will be divided into mono-modal and multi-modal image registration. Since the invention of the medical imaging devices, great diversity of medical imaging sensors have been developed with different physical principles. In practice we have to face the problem of multi-modal registration. In medical image analysis, we often have to consider the images of the human anatomy with deformable characteristics. In order to achieve this goal we need to use the voxel based registration method which considers all of the voxel information of the images in matching. There are several non-rigid registration approaches. However, the variational approach of non-rigid registration can represent the registration problem into a well-posed problem with a well-founded mathematical base. In our work, we considered the forward and backward deformation functions and proposed a variational approach for a new consistent multi-modal non-rigid registration method. By this way, we will find the forward and backward transform to be close to the inverse of each other. This makes the correspondence between two images more consistent and accurate. We use both explicit and implicit difference method to solve the Euler-Lagrange equation and the results show significant improvements in the transformation inverse consistency. Although variational approach for multi-modal non-rigid registration can solve the non-rigid registration problem well, generally speaking, it is slow. The displacement of each voxel has to be calculated and the iteration time is very long since the number of the unknowns are large. Although a multi-resolution strategy can be used to speed up, the registration problem is still slow when registering large medical datasets. The 3D B-spline based method has been used as an efficient method to register medical images since only a small number of control points are used to manipulate the local deformation field. In our work, we developed a 3D B-spline based consistent multi-modal non-rigid registration method with an explicit representation of derivatives. The conventional optimization methods can be used to find the optimal parameters. We use a hierarchical B-spline refinement method to approximate the deformation function from larger to smaller scale. Since the derivatives of the cost function is represented in an explicit way, the computing is reduced. It is more efficient than directly computing the derivative of the cost function by using a numerical evaluation method. The method can be considered as a multi-grid method for solving the consistent variational registration problem. The computing speed is increased by several times. The B-spline based method needs far less iterations to converge as its number of unknowns is small. / Zhang Zhijun. / "October 2005." / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6645. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 209-233). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_343787 |
| Date | January 2005 |
| Contributors | Zhang, Zhijun, Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (xxv, 233 p. : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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