Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatial frequency metrics for analysis of microscopic images of musculoskeletal tissues.

ABSTRACT: Purpose: Imaging-based metrics for analysis of biological tissues are powerful tools that can extract information such as shape, size, periodicity, and many other features to assess the requested qualities of a tissue. Muscular and osseous tissues consist of periodic structures that are directly related to their function, and so analysis of these patterns likely reflects tissue health and regeneration.Methods: A method for assessment of periodic structures is by analyzing them in the spatial frequency domain using the Fourier transform. In this paper, we present two filters which we developed in the spatial frequency domain for the purpose of analyzing musculoskeletal structures. These filters provide information about 1) the angular orientation of the tissues and 2) their periodicity. We explore periodic structural patterns in the mitochondrial network of skeletal muscles that are reflective of muscle metabolism and myogenesis; and patterns of collagen fibers in the bone that are reflective of the organization and health of bone extracellular matrix.Results: We present an analysis of mouse skeletal muscle in healthy and injured muscles. We used a transgenic mouse that ubiquitously expresses fluorescent protein in their mitochondria and performed 2-photon microscopy to image the structures. To acquire the collagen structure of the bone we used non-linear SHG microscopy of mouse flat bone. We analyze and compare juvenile versus adult mice, which have different structural patterns.Conclusions: Our results indicate that these metrics can quantify musculoskeletal tissues during development and regeneration.

SUBMITTER: Forouhesh Tehrani K

PROVIDER: S-EPMC7718369 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Early spatial frequency processing of natural images: an ERP study.

Project description:The present study examined the role of spatial stimulus frequencies in the early visual processing of natural scenes. The content of initially degraded (low- or high-pass filtered) pictures was progressively revealed in a sequence of steps by adding high or low spatial frequencies. Event Related Potentials (ERPs) were used to track the early stages of visual processing. Picture degradation modulated the topography of the P1, with an occipital midline distribution for the most degraded pictures, which became progressively more laterally distributed as pictures became more complete. Picture degradation also modulated the amplitude of the P2. For both low-passed and high-passed scenes, a linear relationship between the spectral power and the amplitude of the P1 and P2 was observed. These results are likely to reflect the progressive engagement of the lateral occipital complex as the amount of information in both the low and high portions of the frequency spectrum increased.

| S-EPMC3669057 | biostudies-literature

XitoSBML: A Modeling Tool for Creating Spatial Systems Biology Markup Language Models From Microscopic Images.

Project description:XitoSBML is a software tool designed to create an SBML (Systems Biology Markup Language) Level 3 Version 1 document from microscopic cellular images. It is implemented as an ImageJ plug-in and is designed to create spatial models that reflect the three-dimensional cellular geometry. With XitoSBML, users can perform spatial model simulations based on realistic cellular geometry by using SBML-supported software tools, including simulators such as Virtual Cell and Spatial Simulator. XitoSBML is open-source and is available at https://github.com/spatialsimulator/XitoSBML/. XitoSBML is confirmed to run on most 32/64-bit operating systems: Windows, MacOS, and Linux.

| S-EPMC6842926 | biostudies-literature

Spatial registration of serial microscopic brain images to three-dimensional reference atlases with the QuickNII tool.

Project description:Modern high throughput brain wide profiling techniques for cells and their morphology, connectivity, and other properties, make the use of reference atlases with 3D coordinate frameworks essential. However, anatomical location of observations made in microscopic sectional images from rodent brains is typically determined by comparison with 2D anatomical reference atlases. A major challenge in this regard is that microscopic sections often are cut with orientations deviating from the standard planes used in the reference atlases, resulting in inaccuracies and a need for tedious correction steps. Overall, efficient tools for registration of large series of section images to reference atlases are currently not widely available. Here we present QuickNII, a stand-alone software tool for semi-automated affine spatial registration of sectional image data to a 3D reference atlas coordinate framework. A key feature in the tool is the capability to generate user defined cut planes through the reference atlas, matching the orientation of the cut plane of the sectional image data. The reference atlas is transformed to match anatomical landmarks in the corresponding experimental images. In this way, the spatial relationship between experimental image and atlas is defined, without introducing distortions in the original experimental images. Following anchoring of a limited number of sections containing key landmarks, transformations are propagated across the entire series of sectional images to reduce the amount of manual steps required. By having coordinates assigned to the experimental images, further analysis of the distribution of features extracted from the images is greatly facilitated.

| S-EPMC6541252 | biostudies-literature

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images.

Project description:The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

| S-EPMC5974206 | biostudies-literature

Objective Morphological Quantification of Microscopic Images Using a Fast Fourier Transform (FFT) Analysis.

Project description:Quantification of immunohistochemistry (IHC) and immunofluorescence (IF) using image intensity depends on a number of variables. These variables add a subjective complexity in keeping a standard within and between laboratories. Fast Fourier Transformation (FFT) algorithms, however, allow for a rapid and objective quantification (via statistical analysis) using cell morphologies when the microscopic structures are oriented or aligned. Quantification of alignment is given in terms of a ratio of FFT intensity to the intensity of an orthogonal angle, giving a numerical value of the alignment of the microscopic structures. This allows for a more objective analysis than alternative approaches, which rely upon relative intensities.

| S-EPMC4849894 | biostudies-literature

Automated analysis of scanning electron microscopic images for assessment of hair surface damage.

Project description:Mechanical damage of hair can serve as an indicator of health status and its assessment relies on the measurement of morphological features via microscopic analysis, yet few studies have categorized the extent of damage sustained, and instead have depended on qualitative profiling based on the presence or absence of specific features. We describe the development and application of a novel quantitative measure for scoring hair surface damage in scanning electron microscopic (SEM) images without predefined features, and automation of image analysis for characterization of morphological hair damage after exposure to an explosive blast. Application of an automated normalization procedure for SEM images revealed features indicative of contact with materials in an explosive device and characteristic of heat damage, though many were similar to features from physical and chemical weathering. Assessment of hair damage with tailing factor, a measure of asymmetry in pixel brightness histograms and proxy for surface roughness, yielded 81% classification accuracy to an existing damage classification system, indicating good agreement between the two metrics. Further ability of the tailing factor to score features of hair damage reflecting explosion conditions demonstrates the broad applicability of the metric to assess damage to hairs containing a diverse set of morphological features.

| S-EPMC7029898 | biostudies-literature

Spatial Image Correlation Spectroscopy (ICS): A Technique for Average Size Determination of Subcellular Accumulated Structures from Fluorescence Microscopic Images.

Project description:Size determination of subcellular structures such as inclusion bodies (IBs) and granules from fluorescent images is important for identification and structural characterization. However, it is often time-consuming just for the comparison of the average size of the structures. Here, we introduce a high-throughput procedure to represent the average size of structures in fluorescent images using Spatial Image Correlation Spectroscopy (SICS). This procedure provides an easier comparison of bodies and granular structures such as inclusion bodies (IBs) including misfolded protein aggregation, granules containing RNA (e.g., stress granules and processing bodies).

| S-EPMC7842500 | biostudies-literature

Microscopic images dataset for automation of RBCs counting.

Project description:A method for Red Blood Corpuscles (RBCs) counting has been developed using RBCs light microscopic images and Matlab algorithm. The Dataset consists of Red Blood Corpuscles (RBCs) images and there RBCs segmented images. A detailed description using flow chart is given in order to show how to produce RBCs mask. The RBCs mask was used to count the number of RBCs in the blood smear image.

| S-EPMC4556816 | biostudies-literature

A universal analysis tool for the detection of asymmetric signal distribution in microscopic images.

Project description:Polarization of tissue is achieved by asymmetric distribution of proteins and organelles within individual cells. However, existing quantitative assays to measure this asymmetry in an automated and unbiased manner suffer from significant limitations.Here, we report a new way to assess protein and organelle localization in tissue based on correlative fluorescence analysis. As a proof of principle, we successfully characterized planar cell polarity dependent asymmetry in developing Drosophila melanogaster tissues on the single cell level using fluorescence cross-correlation.Systematic modulation of signal strength and distribution show that fluorescence cross-correlation reliably detects asymmetry over a broad parameter space. The novel method described here produces robust, rapid, and unbiased measurement of biometrical properties of cell components in live tissue that is readily applicable in other model systems.

| S-EPMC3469164 | biostudies-literature

Semi-automated 3D leaf reconstruction and analysis of trichome patterning from light microscopic images.

Project description:Trichomes are leaf hairs that are formed by single cells on the leaf surface. They are known to be involved in pathogen resistance. Their patterning is considered to emerge from a field of initially equivalent cells through the action of a gene regulatory network involving trichome fate promoting and inhibiting factors. For a quantitative analysis of single and double mutants or the phenotypic variation of patterns in different ecotypes, it is imperative to statistically evaluate the pattern reliably on a large number of leaves. Here we present a method that enables the analysis of trichome patterns at early developmental leaf stages and the automatic analysis of various spatial parameters. We focus on the most challenging young leaf stages that require the analysis in three dimensions, as the leaves are typically not flat. Our software TrichEratops reconstructs 3D surface models from 2D stacks of conventional light-microscope pictures. It allows the GUI-based annotation of different stages of trichome development, which can be analyzed with respect to their spatial distribution to capture trichome patterning events. We show that 3D modeling removes biases of simpler 2D models and that novel trichome patterning features increase the sensitivity for inter-accession comparisons.

| S-EPMC3630213 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data