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ATLAS TO PATIENT REGISTRATION WITH BRAIN TUMOR BASED ON A NEW MESH-FREE METHOD

  • Author / Creator
    Diaz, Idanis Beatriz
  • An atlas is an anatomical representation containing all brain structures well identified in a stereotaxic
    space from a single subject or a population. Atlases provide information about the organization
    and localization of the different brain tissues. One may take advantage of this well-organized information
    for the analysis and processing of a patient’s image by warping the atlas to the patient’s
    image, and establishing a one-to-one correspondence between the two images. This process is also
    known as atlas to patient’s image registration and is quite useful for brain tissues segmentation and
    registering different images to a common reference space. Also, a registered atlas is a model of the
    patient that can be used for simulation of medical procedures, such as recession and needle insertions.
    In the presence of brain tumors, the task of atlas to a patient’s image registration become more
    challenging because tumors deform the brain structures and cause intensity variations in the affected
    areas, augmenting the dissimilarity between the atlas and patient images. As a consequence, most of
    the deformable registration based on intensity or shape similarities between images fail in this cases.
    In order to overcome this issue, some methods involve the use of bio-mechanical models to simulate
    the tumor’s mass-effect and, in this way, can simulate realistic deformation of the brain structure
    in the atlas according to the patient’s image. However, these approaches have some weaknesses,
    mainly related to the assumption of a spherical tumor growth model, the use of Finite Element
    Method to simulate large deformations, and the computational time that they require. We propose a
    new approach for atlas to patient’s image registration with tumor based on bio-mechanical deformation
    of the brain. Contrary to other approaches, our method simulates tumor growth with irregular
    shape by segmenting from the multi-modal magnetic resonance images of a specific patient. We
    have developed a totally new mesh free method for the bio-mechanical deformation avoiding the
    limitations of traditional finite element methods. Experimental results look structurally very similar
    to the patient’s image and outperform two of the top ranking algorithms.

  • Subjects / Keywords
  • Graduation date
    Fall 2015
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3ZK55V4H
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • Carey Jason (Mechanical Engineering)
    • Russell Greiner (Computing Science)
    • Dana Cobzas (Computing Science)
    • Roy Eagleson ( Faculty of Engineering - University of Western Ontario)