Optical flow methods for the registration of compressible flow images and images containing large voxel displacements or artifacts

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dc.contributor.advisorZhang, Yin
dc.creatorCastillo, Edward
dc.date.accessioned2018-12-03T18:32:12Z
dc.date.available2018-12-03T18:32:12Z
dc.date.issued2007
dc.description.abstractThree optical flow image registration (IR) methods referred to as Combined Compressible Local Global (CCLG) optical flow, Large Displacements Optical Flow (LDOF), and Large Displacement Compressible Optical Flow (LDCOF) are introduced. The three novel methods are designed to account for difficulties raised by 4D throacic Computed Tomography (CT) image registration problems, which currently cannot be effectively addressed by existing methods. The 4D CT image registration problem is more challenging than typical IR problems for three key reasons. First, voxel intensities for CT images are proportional to the density of the material imaged. Given that the density of lung tissue changes with respiration, the constant voxel intensity assumption employed by most IR methods is invalid for thoracic CT images. Second, due to the image acquisition procedure, 4D CT image sets are known to suffer from image noise, blurring, and artifacts. Finally, the large size of the image sets requires a computationally efficient and parallelizable algorithm. The CCLG method models compressible image flow with the mass conservation equation coupled with a local-global strategy that alleviates the effects of image noise, and incorporates local image information into the voxel motion model. After a finite element discretization, the resulting large scale linear system is solved using a parallelizable, multi-grid preconditioned conjugate gradient algorithm. The LDOF and LDCOF methods are designed for image sets containing large voxel displacements or erroneous image artifacts. Both methods incorporate unknown image information into the IR problem formulation, which results in a nonlinear least squares problem for both the pixel displacement components and the unknown image values. An alternating linear least squares algorithm is introduced for solving the LDOF and LDCOF nonlinear least squares problems efficiently. After Chapter 1 introduces the basics of IR, the main body of the thesis is divided into two parts. Part 1 is a review of existing IR methodologies. Part 2 derives the three aforementioned new approaches and presents testing results for the three methods, respectively. The computational experiments are carried out on both synthetic and genuine image data. Finally, the thesis concludes in Chapter 8 with a discussion of possible areas of future research.
dc.format.extent136 pp
dc.identifier.callnoTHESIS MATH. SCI. 2008 CASTILLO
dc.identifier.citationCastillo, Edward. "Optical flow methods for the registration of compressible flow images and images containing large voxel displacements or artifacts." (2007) Diss., Rice University. <a href="https://hdl.handle.net/1911/103649">https://hdl.handle.net/1911/103649</a>.
dc.identifier.digital304804861
dc.identifier.urihttps://hdl.handle.net/1911/103649
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectMathematics
dc.subjectRadiology
dc.subjectHealth and environmental sciences
dc.subjectPure sciences
dc.subjectArtifacts
dc.subjectCompressible flow
dc.subjectImage registration
dc.subjectOptical flow
dc.subjectVoxel displacements
dc.titleOptical flow methods for the registration of compressible flow images and images containing large voxel displacements or artifacts
dc.typeThesis
dc.type.materialText
thesis.degree.departmentMathematical Sciences
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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