Three-dimensional digital topological characterization of cancellous bone architecture

ABSTRACT: Cancellous bone consists of a network of bony struts and plates that provide mechanical strength to much of the skeleton at minimum weight. It has been shown that loss in bone mass is accompanied by architectural changes that relate to both scale and topology of the network. In this paper, the concept of three-dimensional (3D) digital topology is presented for characterizing the local topology of each bone voxel after skeletonization of the binary bone images. This method allows us to identify each voxel as belonging to a surface, curve, or junction structure in the trabecular bone network. The method has been quantitatively validated on synthetic images demonstrating its relative immunity to partial volume blurring and noise. Parameters introduced to characterize network topology include surface-to-curve ratio and erosion index. Finally, the technique is shown to quantify the architecture of human trabecular bone in magnetic resonance micro-images acquire

doi:10.1006/cviu.2002.0974 Fuzzy Distance Transform: Theory, Algorithms, and Applications

This paper describes the theory and algorithms of distance transform for fuzzy subsets, called fuzzy distance transform (FDT). The notion of fuzzy distance is formulated by first defining the length of a path on a fuzzy subset and then finding the infimum of the lengths of all paths between two points. The length of a path π in a fuzzy subset of the n-dimensional continuous space ℜ n is defined as the integral of fuzzy membership values along π. Generally, there are infinitely many paths between any two points in a fuzzy subset and it is shown that the shortest one may not exist. The fuzzy distance between two points is defined as the infimum of the lengths of all paths between them. It is demonstrated that, unlike in hard convex sets, the shortest path (when it exists) between two points in a fuzzy convex subset is not necessarily a straight line segment. For any positive number θ ≤ 1, the θ-support of a fuzzy subset is the set of all points in ℜ n with membership values greater than or equal to θ. It is shown that, for any fuzzy subset, for any nonzero θ ≤ 1, fuzzy distance is a metric for the interior of its θ-support. It is also shown that, for any smooth fuzzy subset, fuzzy distance is a metric for the interior of its 0-support (referred to as support). FDT is defined as a process on a fuzzy subset that assigns to a point its fuzzy distance from the complement of the support. The theoretical framework of FDT in continuous space is extended to digital cubic spaces and it is shown that for any fuzzy digital object, fuzzy distance is a metric for the support of the object.