Joint prior models of neighboring objects for 3D image segmentation
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, 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, multiple 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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Conference Proceeding: Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures[show abstract] [hide abstract]
ABSTRACT: This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel density estimation of multiple shapes. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our technique allows simultaneous segmentation of multiple objects, where highly contrasted, easy-to-segment structures can help improve the segmentation of weakly contrasted objects. We demonstrate the effectiveness of our method on both synthetic images and real magnetic resonance images (MRI) for segmentation of basal ganglia structures.Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on; 06/2008