Deformable M-Reps for 3D Medical Image Segmentation.
ABSTRACT M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures - each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.
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ABSTRACT: In this paper, we propose a general tridimensional reconstruction algorithm of range and volumetric images, based on deformable simplex meshes. The algorithm is able to reconstruct surfaces without any restriction on their shape or topology. The different tasks performed during the reconstruction include the segmentation of objects in the scene, the extrapolation of missing data and the control of smoothness, density and geometric quality of the reconstructed model. All surfaces are represented as simplex meshes, that are unstructured meshes whose topology is dual of triangulations. The reconstruction takes place in two stages. First, we initialize the model either manually or using an automatic initialization routine. After the first fit, the topology of the model can be modified by creating holes or increasing its genus. Finally, an iterative adaptation or refinement algorithm decrease the distance of the model from the data while preserving a high geometric and topological quality. W...International Journal of Computer Vision 03/1997; · 3.62 Impact Factor
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ABSTRACT: Object descriptions used for 3D segmentation by deformable models and for statistical characterization of 3D object classes benefit from having intrinsic correspondences over deformation of the objects or multiple instances in the same object class. These correspondences apply over a variety of spatial scale levels and consequently lead to efficient segmentation and probability distributions of geometry that are trainable with an achievable number of training instances. This paper describes a figural coordinate system provided by m-reps models and shows how such coordinates not only provide the required positional correspondences, but also are intuitive and provide orientational and metric correspondences. Examples are given for the segmentation of kidneys from CT and for the statistical characterization of schizophrenia and control classes of cerebral ventricles and of hippocampus pairs.Image and Vision Computing. 01/2002;
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ABSTRACT: A model of object shape by nets of medial and boundary primitives is justified as richly capturing multiple aspects of shape and yet requiring representation space and image analysis work proportional to the number of primitives. Metrics are described that compute an object representation's prior probability of local geometry by reflecting variabilities in the net's links and that compute a likelihood function measuring the degree of match of an image to that object representation. A paradigm for image analysis of deforming such a model to optimize a posterior probability is described, and this paradigm is shown to be usable as a uniform approach for object definition, object-based registration between images of the same or different imaging modalities, and measurement of shape variation of an abnormal anatomical object compared with a normal. Examples of applications of these methods in radiotherapy, surgery, and psychiatry are given. Introduction Our intuition tells us that to segm...11/2000;