Faculty Bibliography

Knowledge about the biological variability of anatomical objects is essential for statistical shape analysis and discrimination between healthy and pathological structures. This paper describes a novel approach that incorporates the variability of an object population into the generation of a characteristic 3D shape model. The proposed shape representation is a coarse-scale sampled medial description derived from a fine-scale spherical harmonics (SPHARM) boundary description. This medial description is composed of a net of medial samples (m-rep) with fixed graph properties. The medial model is computed automatically from a predefined shape space using pruned 3D Voronoi skeletons. A new method determines the stable medial branching topology from the shape space. An intrinsic coordinate system and an implicit correspondence between shapes is defined on the medial manifold. Several studies of biological structures clearly demonstrate that the novel representation has the promise to describe shape changes in a natural and intuitive way. A new medial shape similarity study of group differences between monozygotic and dizygotic twins in lateral ventricle shape demonstrates the meaningful and powerful representation of local and global form.

This paper presents a general framework for analyzing structural and radiometric asymmetry in brain images. In a healthy brain, the left and right hemispheres are largely symmetric across the mid-sagittal plane. Brain tumors may belong to one or both of the following categories: mass-effect, in which the diseased tissue displaces healthy tissue; and infiltrating, in which healthy tissue has become diseased. Mass-effect brain tumors cause structural asymmetry by displacing healthy tissue, and may cause radiometric asymmetry in adjacent normal structures due to edema. Infiltrating tumors have a different radiometric response from healthy tissue. Thus, structural and radiometric asymmetries across the mid-sagittal plane in brain images provide important cues that tumors may be present. We have developed a framework that registers images with their reflections across the mid-sagittal plane. The registration process accounts for tissue displacement through large deformation image warping. Radiometric differences are taken into account through an additive intensity field. We present an efficient multi-scale algorithm for the joint estimation of structural and radiometric asymmetry. Results for nine MR images of patients with tumors and four normal control subjects are presented.

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. (C) 2002 Published by Elsevier Science B.V.