NDLI: 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets

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3D image segmentation of deformable objects with joint shape-intensity prior models using level sets

Content Provider	PubMed Central
Author	Yang, Jing Duncan, James S.
Copyright Year	2004
Abstract	We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the object shape and the image gray levels, along with information derived from the input image, is employed. Our method is motivated by the observation that the shape of an object and the gray level variation in an image have consistent relations that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation model using the joint prior information of the object shape and the image gray levels to realize image segmentation. We introduce a representation for the joint density function of the object and the image gray level values, and define a joint probability distribution over the variations of the object shape and the gray levels contained in a set of training images. By estimating the MAP shape of the object, we formulate the shape-intensity model in terms of level set functions as opposed to landmark points of the object shape. In addition, we evaluate the performance of the level set representation of the object shape by comparing it with the point distribution model (PDM). We found the algorithm to be robust to noise and able to handle multidimensional data, while able to avoid the need for explicit point correspondences during the training phase. Results and validation from various experiments on 2D and 3D medical images are shown.
Related Links	http://dx.doi.org/10.1016/j.media.2004.06.008
Ending Page	294
Page Count	10
Starting Page	285
File Format	PDF
ISSN	13618415
e-ISSN	13618423
Journal	Medical image analysis
Issue Number	3
Volume Number	8
Language	English
Publisher Date	2004-09-01
Access Restriction	Open
Subject Keyword	Radiological and Ultrasound Technology Health Informatics Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition Computer Graphics and Computer-Aided Design Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Radiology, Nuclear Medicine and Imaging Health Informatics Computer Vision and Pattern Recognition Radiological and Ultrasound Technology

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