Shape analysis plays a pivotal role in a large number of applications, ranging from traditional geometry processing to more recent 3D content management. In this scenario, spectral methods are extremely promising as they provide a natural library of tools for shape analysis, intrinsically defined by the shape itself. In particular, the eigenfunctions of the Laplace-Beltrami operator yield a set of real valued functions that provide interesting insights in the structure and morphology of the shape. In this paper, we first analyze different discretizations of the Laplace-Beltrami operator (geometric Laplacians, linear and cubic FEM operators) in terms of the correctness of their eigenfunctions with respect to the continuous case. We then present the family of segmentations induced by the nodal sets of the eigenfunctions, discussing its meaningfulness for shape understanding.

Reeb graphs are compact shape descriptors which convey topological information related to the level sets of a function defined on the shape. Their definition dates back to 1946, and finds its root in Morse theory. Reeb graphs as shape descriptors have been proposed to solve different problems arising in Computer Graphics, and nowadays they play a fundamental role in the field of computational topology for shape analysis. This paper provides an overview of the mathematical properties of Reeb graphs and reconstructs its history in the Computer Graphics context, with an eye towards directions of future research.

High mammographic breast density (H‐MBD) has been associated with increased breast cancer (BC) risk, even after adjustment for established BC risk factors. Only a few studies have examined the influence of diet on MBD. In a longitudinal study in Florence, Italy, we identified about 2,000 women with a mammogram taken 5 years after enrollment, when detailed information on dietary and lifestyle habits and anthropometric measurements had been collected. Original mammograms have been identified and retrieved (1,668; 83%), and MBD was assessed by 2 experienced readers, according to Wolfe's classification and a semiquantitative scale. By logistic analysis, we compared women with H‐MBD (P2 + DY according to Wolfe's classification) with those with low‐MBD (N1 + P1). H‐MBD was confirmed to be inversely associated with BMI, number of children and breast feeding, while it was directly associated with higher educational level, premenopausal status and a previous breast biopsy. In multivariate analyses adjusted for nondietary variables, H‐MBD was inversely associated with increasing consumption of vegetables (p for trend = 0.005) and olive oil (p for trend = 0.04). An inverse association was also evident between H‐MBD and frequent consumption of cheese and high intakes of β‐carotene, vitamin C, calcium and potassium (p for trend ≤ 0.05). On the other hand, we found a positive association with increasing consumption of wine (p for trend = 0.01). This large longitudinal study, the first carried out in Mediterranean women, suggests that specific dietary components may play a key role in determining MBD in this population, thus possibly modulating BC risk. © 2005 Wiley‐Liss, Inc.

Differential topology, and specifically Morse theory, provide a suitable setting for formalizing and solving several problems related to shape analysis. The fundamental idea behind Morse theory is that of combining the topological exploration of a shape with quantitative measurement of geometrical properties provided by a real function defined on the shape. The added value of approaches based on Morse theory is in the possibility of adopting different functions as shape descriptors according to the properties and invariants that one wishes to analyze. In this sense, Morse theory allows one to construct a general framework for shape characterization, parametrized with respect to the mapping function used, and possibly the space associated with the shape. The mapping function plays the role of a lens through which we look at the properties of the shape, and different functions provide different insights. In the last decade, an increasing number of methods that are rooted in Morse theory and make use of properties of real-valued functions for describing shapes have been proposed in the literature. The methods proposed range from approaches which use the configuration of contours for encoding topographic surfaces to more recent work on size theory and persistent homology. All these have been developed over the years with a specific target domain and it is not trivial to systematize this work and understand the links, similarities, and differences among the different methods. Moreover, different terms have been used to denote the same mathematical constructs, which often overwhelm the understanding of the underlying common framework. The aim of this survey is to provide a clear vision of what has been developed so far, focusing on methods that make use of theoretical frameworks that are developed for classes of real functions rather than for a single function, even if they are applied in a restricted manner. The term geometrical-topological used in the title is meant to underline that both levels of information content are relevant for the applications of shape descriptions: geometrical, or metrical, properties and attributes are crucial for characterizing specific instances of features, while topological properties are necessary to abstract and classify shapes according to invariant aspects of their geometry. The approaches surveyed will be discussed in detail, with respect to theory, computation, and application. Several properties of the shape descriptors will be analyzed and compared. We believe this is a crucial step to exploit fully the potential of such approaches in many applications, as well as to identify important areas of future research.