A controlled statistical study to assess measurement variability as a function of test object position and configuration for automated surveillance in a multicenter longitudinal COPD study (SPIROMICS)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134827/1/mp7303.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134827/2/mp7303_am.pd

A Methodology for Evaluating Image Segmentation Algorithms

The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors - precision (reproducibility), accuracy (agreement with truth), and efficiency (time taken) – need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit (FOM), repeat segmentation considering all sources of variation, and determine variations in FOM via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. To assess efficiency, both the computational and the user time required for algorithm and operator training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency are interdependent. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors. The weight given to each factor depends on application

Subchondral bone microarchitecture analysis in the proximal tibia at 7-T MRI

Background Bone remodels in response to mechanical loads and osteoporosis results from impaired ability of bone to remodel. Bone microarchitecture analysis provides information on bone quality beyond bone mineral density (BMD). Purpose To compare subchondral bone microarchitecture parameters in the medial and lateral tibia plateau in individuals with and without fragility fractures. Material and Methods Twelve female patients (mean age = 58 ± 15 years; six with and six without previous fragility fractures) were examined with dual-energy X-ray absorptiometry (DXA) and 7-T magnetic resonance imaging (MRI) of the proximal tibia. A transverse high-resolution three-dimensional fast low-angle shot sequence was acquired (0.234 × 0.234 × 1 mm). Digital topological analysis (DTA) was applied to the medial and lateral subchondral bone of the proximal tibia. The following DTA-based bone microarchitecture parameters were assessed: apparent bone volume; trabecular thickness; profile-edge-density (trabecular bone erosion parameter); profile-interior-density (intact trabecular rods parameter); plate-to-rod ratio; and erosion index. We compared femoral neck T-scores and bone microarchitecture parameters between patients with and without fragility fracture. Results There was no statistical significant difference in femoral neck T-scores between individuals with and without fracture (-2.4 ± 0.9 vs. -1.8 ± 0.7, P = 0.282). Apparent bone volume in the medial compartment was lower in patients with previous fragility fracture (0.295 ± 0.022 vs. 0.317 ± 0.009; P = 0.016). Profile-edge-density, a trabecular bone erosion parameter, was higher in patients with previous fragility fracture in the medial (0.008 ± 0.003 vs. 0.005 ± 0.001) and lateral compartment (0.008 ± 0.002 vs. 0.005 ± 0.001); both P = 0.025. Other DTA parameters did not differ between groups. Conclusion 7-T MRI and DTA permit detection of subtle changes in subchondral bone quality when differences in BMD are not evident

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.