Title page for ETD etd-020509-161314

Document Type	thesis
Author Name	Quartararo, John David
URN	etd-020509-161314
Title	Semi-Automated Segmentation of 3D Medical Ultrasound Images
Degree	MS
Department	Electrical & Computer Engineering
Advisors	Peder C. Pedersen, Advisor David Cyganski, Committee Member Matthew Oliver Ward, Committee Member
Keywords	3d ultrasound ultrasound image processing image segmentation 3d image segmentation medical imaging
Date of Presentation/Defense	2008-10-03
Availability	unrestricted
Abstract A level set-based segmentation procedure has been implemented to identify target object boundaries from 3D medical ultrasound images. Several test images (simulated, scanned phantoms, clinical) were subjected to various preprocessing methods and segmented. Two metrics of segmentation accuracy were used to compare the segmentation results to ground truth models and determine which preprocessing methods resulted in the best segmentations. It was found that by using an anisotropic diffusion filtering method to reduce speckle type noise with a 3D active contour segmentation routine using the level set method resulted in semi-automated segmentation on par with medical doctors hand-outlining the same images.
Files	quartararo.pdf

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