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Anthony Yezzi
Researcher at Georgia Institute of Technology
Publications - 282
Citations - 12523
Anthony Yezzi is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 51, co-authored 269 publications receiving 11931 citations. Previous affiliations of Anthony Yezzi include Washington University in St. Louis & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
A shape-based approach to the segmentation of medical imagery using level sets
Andy Tsai,Anthony Yezzi,William M. Wells,Clare M. Tempany,D. Tucker,Ayres C. Fan,W.E.L. Grimson,Alan S. Willsky +7 more
TL;DR: A parametric model for an implicit representation of the segmenting curve is derived by applying principal component analysis to a collection of signed distance representations of the training data to minimize an objective function for segmentation.
Journal ArticleDOI
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification
TL;DR: The resulting active contour model offers a tractable implementation of the original Mumford-Shah model to simultaneously segment and smoothly reconstruct the data within a given image in a coupled manner and leads to a novel PDE-based approach for simultaneous image magnification, segmentation, and smoothing.
Proceedings ArticleDOI
Gradient flows and geometric active contour models
TL;DR: This paper analyzes geometric active contour models discussed previously from a curve evolution point of view and proposes some modifications based on gradient flows relative to certain new feature-based Riemannian metrics, leading to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well.
Journal ArticleDOI
A geometric snake model for segmentation of medical imagery
TL;DR: This work employs the new geometric active contour models, previously formulated, for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery, and leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well.
Journal ArticleDOI
Conformal curvature flows: From phase transitions to active vision
TL;DR: In this article, the authors analyze geometric active contour models from a curve evolution point of view and propose some modifications based on gradient flows relative to certain new feature-based Riemannian metrics.