We present an extension of the BM3D filter to volumetric data. The proposed algorithm, BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d -dimensional ...patches are stacked together in a ( d +1) -dimensional array and jointly filtered in transform domain. While in BM3D the basic data patches are blocks of pixels, in BM4D we utilize cubes of voxels, which are stacked into a 4-D "group." The 4-D transform applied on the group simultaneously exploits the local correlation present among voxels in each cube and the nonlocal correlation between the corresponding voxels of different cubes. Thus, the spectrum of the group is highly sparse, leading to very effective separation of signal and noise through coefficient shrinkage. After inverse transformation, we obtain estimates of each grouped cube, which are then adaptively aggregated at their original locations. We evaluate the algorithm on denoising of volumetric data corrupted by Gaussian and Rician noise, as well as on reconstruction of volumetric phantom data with non-zero phase from noisy and incomplete Fourier-domain (k-space) measurements. Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in volumetric data reconstruction.
•New experimental measurements of densities are presented for three IGEPAL + MetOH.•Correlation with Tammann-Tait equation and derived properties are calculated.•A highly associative behavior was ...observed for all systems.•Thermodynamic modeling using the PC-SAFT equation of state.
Although physicochemical properties are essential for production and equipment design in oil and gas industries, there is still a lack of data for some systems because of hazardous temperature and pressure conditions. This work provides a volumetric behavior study of three different mixtures composed of nonylphenol ethoxylated nonionic surfactant (IGEPAL CO-520, CO-630, and CO-720) + methanol in the entire mole fraction composition range for temperatures from 313.15 K to 413.15 K and pressures up to 100.0 MPa. These systems behave as a regular liquid, with a volume contraction predominance. It was observed that for high methanol molar content, there is an increase in the cohesive forces because of the ethoxylated chain increases in oxyethylene units. Nevertheless, it was observed that the hydrogen bonds between ether oxygens and hydroxyl hydrogens decrease by increasing temperature, as given by internal pressure data and PC-SAFT modeling. Besides, a new set of parameters for the PC-SAFT equation of state is provided to calculate volumetric and second-order derivative properties.
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Volumetric data abounds in medical imaging and other fields. With the improved imaging quality and the increased resolution, volumetric datasets are getting so large that the existing tools have ...become inadequate for processing and analyzing the data. Here we consider the problem of computing tetrahedral meshes to represent large volumetric datasets with labeled multiple materials, which are often encountered in medical imaging or microscopy optical slice tomography. Such tetrahedral meshes are a more compact and expressive geometric representation so are in demand for efficient visualization and simulation of the data, which are impossible if the original large volumetric data are used directly due to the large memory requirement. Existing methods for meshing volumetric data are not scalable for handling large datasets due to their sheer demand on excessively large run-time memory or failure to produce a tet-mesh that preserves the multi-material structure of the original volumetric data. In this paper we propose a novel approach, called Marching Windows , that uses a moving window and a disk-swap strategy to reduce the run-time memory footprint, devise a new scheme that guarantees to preserve the topological structure of the original dataset, and adopt an error-guided optimization technique to improve both geometric approximation error and mesh quality. Extensive experiments show that our method is capable of processing very large volumetric datasets beyond the capability of the existing methods and producing tetrahedral meshes of high quality.
The histogram of oriented gradients (HOG) is widely used for image description and proves to be very effective. In many vision problems, rotation-invariant analysis is necessary or preferred. Popular ...solutions are mainly based on pose normalization or learning, neglecting some intrinsic properties of rotations. This paper presents a method to build rotation-invariant HOG descriptors using Fourier analysis in polar/spherical coordinates, which are closely related to the irreducible representation of the 2D/3D rotation groups. This is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the Fourier basis (2D) or spherical harmonics (3D). As rotation-invariance is established in an analytical way, we can avoid discretization artifacts and create a continuous mapping from the image to the feature space. In the experiments, we first show that our method outperforms the state-of-the-art in a public dataset for a car detection task in aerial images. We further use the Princeton Shape Benchmark and the SHREC 2009 Generic Shape Benchmark to demonstrate the high performance of our method for similarity measures of 3D shapes. Finally, we show an application on microscopic volumetric data.
•Framework for analysis of colon data.•Automatic extraction of morphological data.•Web-based tool for visual exploration of 2D and 3D data.•Evaluation.
Our paper presents a complete system for the ...data extraction and visual exploration of colon contents and colonmorphological data. The system uses previous research in colon segmentation of MRI images and contributes with anew module for the extraction of morphological data (MGeom), and two modules for the visual exploratory analysis ofthe extracted data (MVisPlot3D and MVisPlot2D).The modules in orange are the ones presented in this paper. In order to extract the colon data (contents andmorphology) we have two different MRI-based sources: T2 and T1-FS images. The two leftmost modules are used tosegment the T2 and T1-FS images. The second column of modules shows the pieces of our framework intended toextract morphological (top) and colon contents (bottom) data. The rightmost modules are used for the visualinspection of the extracted data, as well as other data that we can have as input (e.g. motility data). Display omitted
Computerized Tomography (CT) and, more recently, Magnetic Resonance Imaging (MRI) have become the state-of-the art techniques for morpho-volumetric analysis of abdominal cavities. Due to its constant motility, the colon is an organ difficult to analyze. Unfortunately, CT’s radiative nature makes it only indicated for patients with important disorders. Lately, acquisition techniques that rely on the use of MRI have matured enough to enable the analysis of colon data. This allows gathering data of patients without preparation (i.e. administration of drugs or contrast agents), and incorporating data of patients with non life-threatening diseases and healthy subjects to databases. In this paper we present an end-to-end framework that comprises all the steps to extract colon content and morphology data coupled with a web-based visualization tool that facilitates the visual exploration of such data. We also introduce the set of tools for the extraction of morphological data, and a detailed description of a specifically-designed interactive tool that facilitates a visual comparison of numerical variables within a set of patients, as well as a detailed inspection of an individual. Our prototype was evaluated by domain experts, which showed that our visual approach may reduce the costly process of colon data analysis. As a result, physicians have been able to get new insights on the effects of diets, and also to obtain a better understanding on the motility of the colon.
•Formulation of image symmetrization using nonrigid image registration.•Improvement of symmetrization outcome using iterative optimization.•Demonstrated use case of distortion correction for ...volumetric photoemission data.•Use of symmetry metrics to evaluate and compare symmetrization outcomes.•Development of open source software package for sharing and reuse.
Image symmetrization is an effective strategy to correct symmetry distortion in experimental data for which symmetry is essential in the subsequent analysis. In the process, a coordinate transform, the symmetrization transform, is required to undo the distortion. The transform may be determined by image registration (i.e. alignment) with symmetry constraints imposed in the registration target and in the iterative parameter tuning, which we call symmetry-guided registration. An example use case of image symmetrization is found in electronic band structure mapping by multidimensional photoemission spectroscopy, which employs a 3D time-of-flight detector to measure electrons sorted into the momentum (kx, ky) and energy (E) coordinates. In reality, imperfect instrument design, sample geometry and experimental settings cause distortion of the photoelectron trajectories and, therefore, the symmetry in the measured band structure, which hinders the full understanding and use of the volumetric band mapping datasets. We demonstrate that symmetry-guided registration can correct the symmetry distortion in the momentum-resolved photoemission patterns. Using proposed symmetry metrics, we show quantitatively that the iterative approach to symmetrization outperforms its non-iterative counterpart in the restored symmetry of the outcome while preserving the average shape of the photoemission pattern. Our approach is generalizable to distortion corrections in different types of symmetries and should also find applications in other experimental methods that produce images with similar features.
Reverse Engineering (RE) has been widely used to extract geometric design information from a physical product for reproduction or redesign purposes. A scan of an object is often implemented to ...(re-)construct the computer-aided design model. However, this model is most likely an inaccurate representation of the original design, due to the existing uncertainties in each part and the scanning process. This randomness can result in shrinking the original tolerance region or even yielding asymmetric tolerance regions, which can call for unnecessarily high precision reproduction. In this article, we first propose an algorithm to generate the mean configuration based on the data clouds collected from several scans and multiple parts (if applicable). A Bayesian model with prior knowledge of production processes and scanners is specified to model the statistical properties of the mean configuration. Its marginal posterior outperforms single-scan models with lower variances, concentrating around the physical object or initial design. Furthermore, we propose a bi-objective optimization model to address RE process planning questions regarding the required number of scans and parts to achieve target accuracy requirements. Simulations and industrial case studies, including both unique freeform objects and mechanical parts, are conducted to illustrate and evaluate the performances of proposed methods.
Optical coherence tomography (OCT) can perform non-invasive high-resolution three-dimensional (3D) imaging and has been widely used in biomedical fields, while it is inevitably affected by coherence ...speckle noise which degrades OCT imaging performance and restricts its applications. Here we present a novel speckle-free OCT imaging strategy, named toward-ground-truth OCT (<inline-formula> <tex-math notation="LaTeX">{t} </tex-math></inline-formula>GT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed <inline-formula> <tex-math notation="LaTeX">{t} </tex-math></inline-formula>GT-OCT utilizes an unsupervised 3D-convolution deep-learning network trained using random 3D volumetric data to distinguish and separate speckle from real structures in 3D imaging volumetric space; moreover, <inline-formula> <tex-math notation="LaTeX">{t} </tex-math></inline-formula>GT-OCT effectively further reduces speckle noise and reveals structures that would otherwise be obscured by speckle noise while preserving spatial resolution. Results derived from different samples demonstrated the high-quality speckle-free 3D imaging performance of <inline-formula> <tex-math notation="LaTeX">{t} </tex-math></inline-formula>GT-OCT and its advancement beyond the previous state-of-the-art. The code is available online: https://github.com/Voluntino/tGT-OCT .
Additive Manufacturing (AM) has recently attracted increasing attention among manufacturing industries. This class of technologies is capable of creating parts with complex shapes and intricate ...structures. However, the poor geometric quality of the parts they produced is a major constraint in wide industrial adoption. Currently available analytical techniques based on classic measurement equipment could fail in analyzing the process parameters based on AM-created parts because of the layer-by-layer fabrication process. In this article, we introduce a novel three-dimensional point-cloud-based analytical toolset, volumetric data analysis (VDA), for AM-oriented metrological and experimental analysis. Each step of the VDA is discussed in detail. A high dimensional hypothesis testing procedure is proposed to compare the geometric precision of the part samples from two printing settings. New visualization tools for deviation diagnostics are presented to aid in interpreting and comparing the process outputs. The proposed methods are illustrated with a real experiment to compare the effects of different layer thicknesses in a filament deposition modeling printing process.
•A statistical framework is proposed to analyze three-dimensional point cloud samples.•A new hypothesis testing procedure is proposed for high-dimensional mean configurations of additive manufactured products.•Layer thickness has a strong impact on the precision of the parts produced by the filament deposition modeling process.•A shape deviation diagnostic tool is designed for process knowledge discovery.
With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in ...medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.
•Lack of a systematic review of works on synthetic volumetric data generation.•Volumetric data is very important in several fields, especially in medicine.•Rare diseases, privacy issues and high cost can lead to restricted data availability.•We outline GAN-based methods in these areas with common architectures, loss and evaluation metrics, advantages, and disadvantages, taxonomy, evaluations, challenges, and research opportunities.
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