4,631 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
A Bayesian Nonparametric model for textural pattern heterogeneity
Cancer radiomics is an emerging discipline promising to elucidate lesion
phenotypes and tumor heterogeneity through patterns of enhancement, texture,
morphology, and shape. The prevailing technique for image texture analysis
relies on the construction and synthesis of Gray-Level Co-occurrence Matrices
(GLCM). Practice currently reduces the structured count data of a GLCM to
reductive and redundant summary statistics for which analysis requires variable
selection and multiple comparisons for each application, thus limiting
reproducibility. In this article, we develop a Bayesian multivariate
probabilistic framework for the analysis and unsupervised clustering of a
sample of GLCM objects. By appropriately accounting for skewness and
zero-inflation of the observed counts and simultaneously adjusting for existing
spatial autocorrelation at nearby cells, the methodology facilitates estimation
of texture pattern distributions within the GLCM lattice itself. The techniques
are applied to cluster images of adrenal lesions obtained from CT scans with
and without administration of contrast. We further assess whether the resultant
subtypes are clinically oriented by investigating their correspondence with
pathological diagnoses. Additionally, we compare performance to a class of
machine-learning approaches currently used in cancer radiomics with simulation
studies.Comment: 45 pages, 7 figures, 1 Tabl
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