Multiple Scales Of Resolution Research Articles

Multi-Scale Image- and Feature-Level Alignment for Cross-Resolution Person Re-Identification

Cross-Resolution Person Re-Identification (re-ID) aims to match images with disparate resolutions arising from variations in camera hardware and shooting distances. Most conventional works utilize Super-Resolution (SR) models to recover Low Resolution (LR) images to High Resolution (HR) images. However, because the SR models cannot completely compensate for the missing information in the LR images, there is still a large gap between the HR image recovered from the LR images and the real HR images. To tackle this challenge, we propose a novel Multi-Scale Image- and Feature-Level Alignment (MSIFLA) framework to align the images on multiple resolution scales at both the image and feature level. Specifically, (i) we design a Cascaded Multi-Scale Resolution Reconstruction (CMSR2) module, which is composed of three cascaded Image Reconstruction (IR) networks, and can continuously reconstruct multiple variables of different resolution scales from low to high for each image, regardless of image resolution. The reconstructed images with specific resolution scales are of similar distribution; therefore, the images are aligned on multiple resolution scales at the image level. (ii) We propose a Multi-Resolution Representation Learning (MR2L) module which consists of three-person re-ID networks to encourage the IR models to preserve the ID-discriminative information during training separately. Each re-ID network focuses on mining discriminative information from a specific scale without the disturbance from various resolutions. By matching the extracted features on three resolution scales, the images with different resolutions are also aligned at the feature-level. We conduct extensive experiments on multiple public cross-resolution person re-ID datasets to demonstrate the superiority of the proposed method. In addition, the generalization of MSIFLA in handling cross-resolution retrieval tasks is verified on the UAV vehicle dataset.

Reconstituting Cytoarchitecture and Function of Human Epithelial Tissues on an Open-Top Organ-Chip

Nearly all human organs are lined with epithelial tissues, comprising one or multiple layers of tightly connected cells organized into three-dimensional (3D) structures. One of the main functions of epithelia is the formation of barriers that protect the underlining tissues against physical and chemical insults and infectious agents. In addition, epithelia mediate the transport of nutrients, hormones, and other signaling molecules, often creating biochemical gradients that guide cell positioning and compartmentalization within the organ. Owing to their central role in determining organ-structure and function, epithelia are important therapeutic targets for many human diseases that are not always captured by animal models. Besides the obvious species-to-species differences, conducting research studies on barrier function and transport properties of epithelia in animals is further compounded by the difficulty of accessing these tissues in a living system. While two-dimensional (2D) human cell cultures are useful for answering basic scientific questions, they often yield poor in vivo predictions. To overcome these limitations, in the last decade, a plethora of micro-engineered biomimetic platforms, known as organs-on-a-chip, have emerged as a promising alternative to traditional in vitro and animal testing. Here, we describe an Open-Top Organ-Chip (or Open-Top Chip), a platform designed for modeling organ-specific epithelial tissues, including skin, lungs, and the intestines. This chip offers new opportunities for reconstituting the multicellular architecture and function of epithelial tissues, including the capability to recreate a 3D stromal component by incorporating tissue-specific fibroblasts and endothelial cells within a mechanically active system. This Open-Top Chip provides an unprecedented tool for studying epithelial/mesenchymal and vascular interactions at multiple scales of resolution, from single cells to multi-layer tissue constructs, thus allowing molecular dissection of the intercellular crosstalk of epithelialized organs in health and disease.

Restoring morphology of light sheet microscopy data based on magnetic resonance histology.

The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density.

Petrophysical analysis and mudstone lithofacies classification of the HRZ shale, North Slope, Alaska

Mudstone is vertically and laterally heterogeneous in terms of variations in mineral composition, organic carbon content, and petrophysical properties. The purpose of this study is to classify mudstone lithofacies within the High Radioactive Zone (HRZ) Shale on the North Slope, Alaska, based upon mineralogy, total organic carbon (TOC), and petrophysical properties to interpret their distributions and depositional/diagenetic environments. We integrate core data from six wells and well logs from 18 wells to analyze the variations in rock properties in the HRZ Shale. Analysis of cores at multiple scales of resolution by techniques, including X-ray diffraction (XRD), trace element geochemistry, pyrolysis, and scanning electron microscopy (SEM) are integrated with conventional and advanced wireline logging suites (including pulsed neutron spectroscopy [PNS]) to map vertical and lateral variation in mudstone lithofacies across the study area. Results show that the HRZ Shale exhibits a high degree of vertical and lateral heterogeneities in mineralogy, TOC, and petrophysical properties. This shale formation is composed of 12 lithofacies: pyritic organic mixed shale, organic mixed shale, pyritic mixed shale, mixed shale, pyritic organic mudstone, organic mudstone, pyritic grey mudstone, grey mudstone, pyritic organic siliceous shale, organic siliceous shale, pyritic siliceous shale, and siliceous shale. Quartz abundance is generally higher in the west, and clay proportions increase from the southwest to the northeast of the study area. The TOC content is variable across the HRZ Shale, ranging from 1.5 to 15 wt%. The lithofacies are influenced by depositional conditions, diagenesis, redox conditions, organic matter productivity, and preservation.

Probing β-Cell Biology in Space and Time.

β-Cells in the islet of Langerhans have a central role in maintaining energy homeostasis. Understanding the physiology of β-cells and other islet cells requires a deep understanding of their structural and functional organization, their interaction with vessels and nerves, the layout of paracrine interactions, and the relationship between subcellular compartments and protein complexes inside each cell. These elements are not static; they are dynamic and exert their biological actions at different scales of time. Therefore, scientists must be able to investigate (and visualize) short- and long-lived events within the pancreas and β-cells. Current technological advances in microscopy are able to bridge multiple spatiotemporal scales in biology to reveal the complexity and heterogeneity of β-cell biology. Here, I briefly discuss the historical discoveries that leveraged microscopes to establish the basis of β-cell anatomy and structure, the current imaging platforms that allow the study of islet and β-cell biology at multiple scales of resolution, and their challenges and implications. Lastly, I outline how the remarkable longevity of structural elements at different scales in biology, from molecules to cells to multicellular structures, could represent a previously unrecognized organizational pattern in developing and adult β-cells and pancreas biology.

China Population Distributions at Multiple Geographical Scales and Their Correlates

Most population distribution studies have focused on a single spatial resolution scale, thus, leading to a limited representation of the “real-world”. The present work, instead, proposes a “zoom lens” approach to detect and remove localized variation while retaining the general population distribution trends at multiple resolution scales. Different “focal length optics” are able to eliminate the unnecessary spatial details and filter out the underlined trend at the specified resolution. As spatial resolution scale decreases, the general trend and local variation of population distribution can be identified from the fine resolution to the national level. On the basis of smallscale analysis, it was shown that high-density population in China is roughly aligned with an oblique trend line along the Heihe–Tengchong Line, which provides a mathematical foundation for it. It was also found that the positive relationship between correlates and population distribution became more significant at the national scale. Topographic elevation has the largest negative impact on population distribution at the country level, whereas water accessibility has the largest effect on population distribution at any resolution. Furthermore, by combining the “zoom lens” approach with geographic weighted regression, the population distribution correlates (main roads, railways, live green vegetation, elevation, relief amplitude, rivers and lakes) were studied. A significant deterioration of accessibility to main roads and water in certain areas was identified at the national scale, which was not detected without “zoom lens” approach. Therefore, this study demonstrated that correlation or any other relationship may vary at different spatial scales of study.

Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age

We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs. female groups, as well as correlations with age (corrected p < 0.05). The open-access brain imaging data exchange (ABIDE) brain MRI dataset is used to evaluate texture features derived from 31 brain regions from 1112 subjects including 573 typically developing control (TDC, 99 females, 474 males) and 539 Autism spectrum disorder (ASD, 65 female and 474 male) subjects. Statistically significant texture differences between ASD vs. TDC groups are identified asymmetrically in the right hippocampus, left choroid-plexus and corpus callosum (CC), and symmetrically in the cerebellar white matter. Sex-related texture differences in TDC subjects are found in primarily in the left amygdala, left cerebellar white matter, and brain stem. Correlations between age and texture in TDC subjects are found in the thalamus-proper, caudate and pallidum, most exhibiting bilateral symmetry.

A new method combining enhanced resolution and pattern identification

We present a simple and general-purpose method able to combine high-resolution procedure with the classification and identification of objects of interest from microscopy imaging. The method is composed of two stages. First (pattern recognition), promising components (possible objects of interest) in the image are detected and small regions containing the objects of interest are extracted using a feature finder. Second, high-resolution algorithms are applied to such identified components in order to approach a multiple scales of resolution. Although the method is indeed to be applied to any microscopy technique, in this paper, we have focused the attention on biological systems, like animal cells, recorded with an atomic force microscopy.

Selected Applications of Scale Spaces in Microscopic Image Analysis

Abstract Image segmentation methods can be classified broadly into two classes: intensity-based and geometry-based. Edge detection is the base of many geometry-based segmentation approaches. Scale space theory represents a systematic treatment of the issues of spatially uncorrelated noise with its main application being the detection of edges, using multiple resolution scales, which can be used for subsequent segmentation, classification or encoding. The present paper will give an overview of some recent applications of scale spaces into problems of microscopic image analysis. Particular overviews will be given to Gaussian and alpha-scale spaces. Some applications in the analysis of biomedical images will be presented. The implementation of filters will be demonstrated.

Cross-species approaches to cognitive neuroplasticity research

Neuroplasticity studies investigate the neural mechanisms that support learning-induced changes in cognition and behavior. These studies are performed in both experimental animals and humans across development from childhood to aging. Here, we review select recent studies that have sought to combine both animal and human neuroplasticity research within the same study. In investigating the same cognitive/behavioral functions in parallel in animals and humans, these studies take advantage of complementary neuroscience research methods that have been established for each species. In animals, these methods include investigations of genetic and molecular biomarker expression and micro-scale electrophysiology in single neurons in vivo or in brain slices. In humans, these studies assess macro-scale neural network dynamics using neuroimaging methods including EEG (electroencephalography) and functional and structural MRI (magnetic resonance imaging). Thus, by combining these diverse and complementary methodologies cross-species studies have the unique ability to bridge molecular, systems and cognitive neuroscience research. Additionally, they serve a vital role in translational neuroscience, providing a direct bridge between animal models and human neuropsychiatric disorders. Comprehensive cross-species understanding of neural mechanisms at multiple scales of resolution and how these neural dynamics relate to behavioral outcomes, then serve to inform development and optimization of treatment strategies.

Temperature variation aware multi-scale delay, power and thermal analysis at RT and gate level

Thermal effects are rapidly gaining importance in nanometer CMOS technologies. Increased power density, coupled with spatio-temporal variability of chip workloads, causes on-die temperature non-uniformities. The assumption of a uniform temperature for the delay and power analysis of a large CMOS circuit produces inaccurate results. For this reason, significant design margins are taken to ensure safe operation. To improve design quality, we need precise localization of hotspots at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis needs to be done at multiple levels of granularity using a versatile thermal floorplan. We propose MiMAPT, an approach for analyzing delay, power and temperature in digital circuits. MiMAPT integrates seamlessly into major industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Thermal analysis is done at register-transfer (RT) and then gate-level considering non-regular shapes of on-die units with multiple scales of resolution and speed. To demonstrate the capability of MiMAPT in temperature variation aware delay/power estimation, a widely used IP block is chosen and four different chips are implemented using 65nm and 40nm (LVT, HVT) technology nodes. Different temperature patterns are then applied to the design. Accuracy improvements of up to 28% for static power and 16% for minimum clock period are reported in comparison with uniform averaged temperature assumption. Evaluating the ability of MiMAPT in multi-scale thermal analysis, a speed-up of 98× is reported compared to fine-grained method, while keeping false negatives at zero and the error of temperature estimation below 0.05°C.

Quantitative immunocytochemistry at the ultrastructural level: a stereology-based approach to molecular nanomorphomics.

Biological systems span multiple levels of structural organisation from the macroscopic, via the microscopic, to the nanoscale. Therefore, comprehensive investigation of systems biology requires application of imaging modalities that reveal structure at multiple resolution scales. Nanomorphomics is the part of morphomics devoted to the systematic study of functional morphology at the nanoscale and an important element of its achievement is the combination of immunolabelling and transmission electron microscopy (TEM). The ultimate goal of quantitative immunocytochemistry is to estimate numbers of target molecules (usually peptides, proteins or protein complexes) in biological systems and to map their spatial distributions within them. Immunogold cytochemistry utilises target-specific affinity markers (primary antibodies) and visualisation aids (e.g., colloidal gold particles or silver-enhanced nanogold particles) to detect and localise target molecules at high resolution in intact cells and tissues. In the case of post-embedding labelling of ultrathin sections for TEM, targets are localised as a countable digital readout by using colloidal gold particles. The readout comprises a spatial distribution of gold particles across the section and within the context of biological ultrastructure. The observed distribution across structural compartments (whether volume- or surface-occupying) represents both specific and non-specific labelling; an assessment by eye alone as to whether the distribution is random or non-random is not always possible. This review presents a coherent set of quantitative methods for testing whether target molecules exhibit preferential and specific labelling of compartments and for mapping the same targets in two or more groups of cells as their TEM immunogold-labelling patterns alter after experimental manipulation. The set also includes methods for quantifying colocalisation in multiple-labelling experiments and mapping absolute numbers of colloidal gold particles across compartments at specific positions within cells having a point-like inclusion (e.g., centrosome, nucleolus) and a definable vertical axis. Although developed for quantifying colloidal gold particles, the same methods can in principle be used to quantify other electron-dense punctate nanoparticles, including quantum dots.

Integrated photoacoustic, ultrasound and fluorescence platform for diagnostic medical imaging-proof of concept study with a tissue mimicking phantom.

The structural and molecular heterogeneities of biological tissues demand the interrogation of the samples with multiple energy sources and provide visualization capabilities at varying spatial resolution and depth scales for obtaining complementary diagnostic information. A novel multi-modal imaging approach that uses optical and acoustic energies to perform photoacoustic, ultrasound and fluorescence imaging at multiple resolution scales from the tissue surface and depth is proposed in this paper. The system comprises of two distinct forms of hardware level integration so as to have an integrated imaging system under a single instrumentation set-up. The experimental studies show that the system is capable of mapping high resolution fluorescence signatures from the surface, optical absorption and acoustic heterogeneities along the depth (>2cm) of the tissue at multi-scale resolution (<1µm to <0.5mm).

Integrated Optical Coherence Tomography and Microscopy for Ex Vivo Multiscale Evaluation of Human Breast Tissues

Three-dimensional (3D) tissue imaging methods are expected to improve surgical management of cancer. In this study, we examined the feasibility of two 3D imaging technologies, optical coherence tomography (OCT) and optical coherence microscopy (OCM), to view human breast specimens based on intrinsic optical contrast. Specifically, we imaged 44 ex vivo breast specimens including 34 benign and 10 malignant lesions with an integrated OCT and OCM system developed in our laboratory. The system enabled 4-μm axial resolution (OCT and OCM) with 14-μm (OCT) and 2-μm (OCM) transverse resolutions, respectively. OCT and OCM images were compared with corresponding histologic sections to identify characteristic features from benign and malignant breast lesions at multiple resolution scales. OCT and OCM provide complimentary information about tissue microstructure, thus showing distinctive patterns for adipose tissue, fibrous stroma, breast lobules and ducts, cysts and microcysts, as well as in situ and invasive carcinomas. The 3D imaging capability of OCT and OCM provided complementary information to individual 2D images, thereby allowing tracking features from different levels to identify low-contrast structures that were difficult to appreciate from single images alone. Our results lay the foundation for future in vivo optical evaluation of breast tissues, using OCT and OCM, which has the potential to guide core needle biopsies, assess surgical margins, and evaluate nodal involvement in breast cancer.

Bayesian Multiscale Multiple Imputation With Implications for Data Confidentiality

Many scientific, sociological, and economic applications present data that are collected on multiple scales of resolution. One particular form of multiscale data arises when data are aggregated across different scales both longitudinally and by economic sector. Frequently, such datasets experience missing observations in a manner that they can be accurately imputed, while respecting the constraints imposed by the multiscale nature of the data, using the method we propose known as Bayesian multiscale multiple imputation. Our approach couples dynamic linear models with a novel imputation step based on singular normal distribution theory. Although our method is of independent interest, one important implication of such methodology is its potential effect on confidential databases protected by means of cell suppression. In order to demonstrate the proposed methodology and to assess the effectiveness of disclosure practices in longitudinal databases, we conduct a large-scale empirical study using the U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW). During the course of our empirical investigation it is determined that several of the predicted cells are within 1% accuracy, thus causing potential concerns for data confidentiality.

Statistical Considerations and Techniques for Understanding Physiological Data, Modeling, and Treatments

Comparing models with data always forces us to deal with uncertainty. This uncertainty may take many different forms and involve multiple scales of resolution in the model and in the experiment. In this paper, we discuss issues surrounding the development of deterministic dynamic models of mean behavior and the associated statistical models of the difference between model and experiment. We touch on a variety of topics, including basic exploratory data analysis, confidence bounds and model reduction hypothesis tests. Tools ranging from nonlinear regression to time series to Bayesian decision theory are presented.

Automatic recognition of biological particles in microscopic images

A simple and general-purpose system to recognize biological particles is presented. It is composed of four stages: First (if necessary) promising locations in the image are detected and small regions containing interesting samples are extracted using a feature finder. Second, differential invariants of the brightness are computed at multiple scales of resolution. Third, after point-wise non-linear mappings to a higher dimensional feature space, this information is averaged over the whole region thus producing a vector of features for each sample that is invariant with respect to rotation and translation. Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model. This system was developed to classify 12 categories of particles found in human urine; it achieves a 93.2% correct classification rate in this application. It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83% correct classification rate for the three categories found during one month of observation. Pollen classification is challenging even for human experts and this performance is considered good.

Scale-space analysis of time series in circulatory research

Statistical analysis of time series is still inadequate within circulation research. With the advent of increasing computational power and real-time recordings from hemodynamic studies, one is increasingly dealing with vast amounts of data in time series. This paper aims to illustrate how statistical analysis using the significant nonstationarities (SiNoS) method may complement traditional repeated-measures ANOVA and linear mixed models. We applied these methods on a dataset of local hepatic and systemic circulatory changes induced by aortoportal shunting and graded liver resection. We found SiNoS analysis more comprehensive when compared with traditional statistical analysis in the following four ways: 1) the method allows better signal-to-noise detection; 2) including all data points from real time recordings in a statistical analysis permits better detection of significant features in the data; 3) analysis with multiple scales of resolution facilitates a more differentiated observation of the material; and 4) the method affords excellent visual presentation by combining group differences, time trends, and multiscale statistical analysis allowing the observer to quickly view and evaluate the material. It is our opinion that SiNoS analysis of time series is a very powerful statistical tool that may be used to complement conventional statistical methods.

Localization of corresponding points in fingerprints by complex filtering

For the alignment of two fingerprints certain landmark points are needed. These should be automaticaly extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (singular points, SPs) in the fingerprints. We identify an SP by its symmetry properties. SPs are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filters, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

The automated extraction of environmentally relevant features from digital imagery using Bayesian multi-resolution analysis

In this paper, we discuss the use of hierarchical tree-structured Bayesian networks for integrating knowledge concerning contextual relationships between environmentally relevant features extracted from digital imagery at multiple resolution scales. In our model, conditional probability distributions over continuous valued observations are parameterized using a mixture of multivariate Gaussian distributions. Separate classifiers for pixels and groups of pixels are used as sub-components of the overall model. The Bayesian formalism allows models to be composed in a systematic and statistically sound manner. We illustrate how this approach can be used to resolve ambiguity leading to classification errors and thus improve techniques for the classification of land use from aerial imagery. We present an example relevant to ecosystem analysis, the monitoring of urban growth and the automatic generation of input parameters for hydrologic models.