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
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.
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