Article

Joint Prior Models of Neighboring Objects for 3D Image Segmentation.

Departments of Electrical Engineering and Diagnostic Radiology, Yale University P.O. Box 208042, New Haven CT 06520-8042, USA.
Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 06/2004; 1:I314-I319. DOI: 10.1109/CVPR.2004.1315048
Source: PubMed

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.

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    • "To the best of our knowledge, our approach is the first scheme of multi-structure (object) segmentation employing coupled nonparametric shape and relative pose priors. As compared to existing methods [43], [45], which are based on multi-object priors, our approach takes advantage of nonparametric density estimates in order to capture non-linear shape variability. We demonstrate the effectiveness of our approach on volumetric segmentations in real MR images accompanied by a quantitative analysis of the segmentation accuracy. "
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    12/2010; 29(12):1959-78. DOI:10.1109/TMI.2010.2053554
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    • "Bazin and Pham (2007) proposed an atlas-based segmentation method that uses topological constraints to avoid possible bias introduced by the atlas . Yang and Duncan (2004) employed manually labeled structures to support automatic segmentation of neighboring structures within the same image. Tu et al. (2008) proposed a discriminative approach for segmentation of adjacent brain structures using a set of features learned from training examples. "
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    Medical image analysis 10/2010; 14(5):654-65. DOI:10.1016/j.media.2010.05.004 · 3.68 Impact Factor
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    • "To the best of our knowledge, our approach is the first scheme of multi-structure (object) segmentation employing coupled nonparametric shape and relative pose priors. As compared to existing methods [43], [45], which are based on multi-object priors, our approach takes advantage of nonparametric density estimates in order to capture non-linear shape variability. We demonstrate the effectiveness of our approach on volumetric segmentations in real MR images accompanied by a quantitative analysis of the segmentation accuracy. "
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