Type of Document Master's Thesis Author Cois, C. Aaron URN etd-05162006-155257 Title Shells and Spheres: A Novel Framework for Variable Scale Statistical Image Analysis Degree Master of Science Program Computer Science School School of Arts and Sciences Advisory Committee
Advisor Name Title Dr. George Stetten Committee Co-Chair Dr. Milos Hauskrecht Committee Co-Chair Dr. C. C. Li Committee Member Keywords
- medical imaging
- medical image analysis
- image processing
- computer vision
- image segmentation
Date of Defense 2006-05-09 Availability unrestricted Abstract A framework for analyzing images, called emph{Shells and Spheres},has been developed based on a set of spheres with adjustable radii,
with exactly one sphere centered at each image pixel. This set of
spheres, known as a emph{sphere map}, is considered optimized when
each sphere reaches, but does not cross, the nearest boundary.
Calculations denoted as emph{Variable-Scale Statistics} (VSS) are
performed on populations of pixels within spheres, as well as
populations of adjacent and overlapping spheres, in order to deduce
the proper radius of each sphere. Spheres grow or shrink by adding
or deleting an outer shell one pixel thick . Unlike conventional
fixed-scale kernels, our spherical operators consider as many pixels
as possible to differentiate between objects and accurately
delineate boundaries. The term ``sphere" is used for brevity, though
the approach is not limited to 3D and is valid in $n$-dimensions.
The approach is illustrated using both real images and noiseless
synthetic images containing objects with uniform intensity, and more
closely examined and validated using various synthetic images with
added white noise and multiple contrast enhanced CT scans of the
aortic arch. A particular algorithm using Shells and Spheres is
described and demonstrated on segmentation of the aortic arch in a
contrast-enhanced CT scan, both in 2D and 3D.
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