Invention Grant
US07773806B2 Efficient kernel density estimation of shape and intensity priors for level set segmentation
失效
用于水平集分割的形状和强度优先级的有效核密度估计
- Patent Title: Efficient kernel density estimation of shape and intensity priors for level set segmentation
- Patent Title (中): 用于水平集分割的形状和强度优先级的有效核密度估计
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Application No.: US11397040Application Date: 2006-04-03
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Publication No.: US07773806B2Publication Date: 2010-08-10
- Inventor: Daniel Cremers , Mikael Rousson
- Applicant: Daniel Cremers , Mikael Rousson
- Applicant Address: US PA Malvern
- Assignee: Siemens Medical Solutions USA, Inc.
- Current Assignee: Siemens Medical Solutions USA, Inc.
- Current Assignee Address: US PA Malvern
- Agent Donald B. Paschburg
- Main IPC: G06K9/34
- IPC: G06K9/34 ; G06K9/62
Abstract:
Methods and systems for image segmentation are disclosed. A nonlinear statistical shape model of an image is integrated with a non-parametric intensity model to estimate characteristics of an image and create segmentations of an image based on Bayesian inference from characteristics of prior learned images based on the same models.
Public/Granted literature
- US20070003137A1 Efficient kernel density estimation of shape and intensity priors for level set segmentation Public/Granted day:2007-01-04
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