NDLI: Joint Prior Models of Neighboring Objects for 3D Image Segmentation

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Joint Prior Models of Neighboring Objects for 3D Image Segmentation

Content Provider	PubMed Central
Author	Yang, Jing Duncan, James S.
Copyright Year	2004
Abstract	This paper presents a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of multiple objects, along with information derived from the input image, is employed. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. In contrast to the work presented earlier in [1], we define a Maximum A Posteriori (MAP) estimation model using the joint prior information of the multiple objects to realize image segmentation, which allows multiple objects with clearer boundaries to be reference objects to provide constraints in the segmentation of difficult objects. To achieve this, muiltiple signed distance functions are employed as representations of the objects in the image. We introduce a representation for the joint density function of the neighboring objects, and define joint probability distribution over the variations of objects contained in a set of training images. By estimating the MAP shapes of the objects, we formulate the joint shape prior models in terms of level set functions. We found the algorithm to be robust to noise and able to handle multidimensional data. Furthermore, it avoids the need for point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
Related Links	http://dx.doi.org/10.1109/cvpr.2004.1315048
Starting Page	314
File Format	PDF
ISSN	10636919
Journal	Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume Number	1
Language	English
Publisher Date	2004-06-27
Access Restriction	Open
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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