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Automated Method for N-Dimensional Shape Detection Based on Medial Image Features

Revanna Shivaprabhu, Vikas (2011) Automated Method for N-Dimensional Shape Detection Based on Medial Image Features. Master's Thesis, University of Pittsburgh.

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    Abstract

    The focus of my thesis is to build upon the method of Shells and Spheres developed in our laboratory. The method as previously implemented extracts medial points based on the divergence of the direction function to the nearest boundary as it changes across medial ridges, and reports the angle between the directions from the medial point to two respective boundary points. The direction function is determined by analyzing the mean and variance of intensity within pairs of adjacent spherical (circular in 2D) regions in the image. My thesis research has involved improving the search method for determining the distance function and identifying medial points, and then clustering those medial points to extract features including scale, orientation and medial dimensionality. These are then analyzed to detect local geometric shapes. I have implemented the methods in N dimensions in the Insight Toolkit (ITK). In 3D, the method yields three fundamental dimensionalities of local shape: the sphere, the cylinder, and the slab, which, along with scale, are invariant to translation and rotation. Tests are performed on simple geometric objects including the hollow sphere (slab), torus (cylinder) and sphere. The results confirm the capability of the system to successfully identify the described medial shape features, and lay the foundation for ongoing research in identifying more complex anatomical objects in medical images.


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    Item Type: University of Pittsburgh ETD
    ETD Committee:
    ETD Committee TypeCommittee MemberEmail
    Committee ChairStetten, Georgestetten@andrew.cmu.edu
    Committee CoChairChing-Chung, Liccl@pitt.edu
    Committee MemberMickle, Marlinmickle@pitt.edu
    Committee MemberLoughlin, Patloughlin@pitt.edu
    Title: Automated Method for N-Dimensional Shape Detection Based on Medial Image Features
    Status: Unpublished
    Abstract: The focus of my thesis is to build upon the method of Shells and Spheres developed in our laboratory. The method as previously implemented extracts medial points based on the divergence of the direction function to the nearest boundary as it changes across medial ridges, and reports the angle between the directions from the medial point to two respective boundary points. The direction function is determined by analyzing the mean and variance of intensity within pairs of adjacent spherical (circular in 2D) regions in the image. My thesis research has involved improving the search method for determining the distance function and identifying medial points, and then clustering those medial points to extract features including scale, orientation and medial dimensionality. These are then analyzed to detect local geometric shapes. I have implemented the methods in N dimensions in the Insight Toolkit (ITK). In 3D, the method yields three fundamental dimensionalities of local shape: the sphere, the cylinder, and the slab, which, along with scale, are invariant to translation and rotation. Tests are performed on simple geometric objects including the hollow sphere (slab), torus (cylinder) and sphere. The results confirm the capability of the system to successfully identify the described medial shape features, and lay the foundation for ongoing research in identifying more complex anatomical objects in medical images.
    Date: 26 January 2011
    Date Type: Completion
    Defense Date: 02 December 2010
    Approval Date: 26 January 2011
    Submission Date: 19 November 2010
    Access Restriction: No restriction; Release the ETD for access worldwide immediately.
    Patent pending: No
    Institution: University of Pittsburgh
    Thesis Type: Master's Thesis
    Refereed: Yes
    Degree: MSEE - Master of Science in Electrical Engineering
    URN: etd-11192010-134222
    Uncontrolled Keywords: medial detection; medial manifold; shape detection
    Schools and Programs: Swanson School of Engineering > Electrical Engineering
    Date Deposited: 10 Nov 2011 15:05
    Last Modified: 14 May 2012 11:08
    Other ID: http://etd.library.pitt.edu/ETD/available/etd-11192010-134222/, etd-11192010-134222

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