ImageJ-MATLAB: a bidirectional framework for scientific image analysis interoperability.
Ontology highlight
ABSTRACT: Summary
ImageJ-MATLAB is a lightweight Java library facilitating bi-directional interoperability between MATLAB and ImageJ. By defining a standard for translation between matrix and image data structures, researchers are empowered to select the best tool for their image-analysis tasks.Availability and implementation
Freely available extension to ImageJ2 ( http://imagej.net/Downloads ). Installation and use instructions available at http://imagej.net/MATLAB_Scripting. Tested with ImageJ 2.0.0-rc-54 , Java 1.8.0_66 and MATLAB R2015b.Contact
eliceiri@wisc.edu.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Hiner MC
PROVIDER: S-EPMC6041959 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BACKGROUND:ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. RESULTS:We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called "ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. CONCLUSIONS:Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ's development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.
| S-EPMC5708080 | biostudies-other
Project description:For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
| S-EPMC5554542 | biostudies-other
Project description:SummaryFunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis.Availability and implementationFreely available plugin to Fiji (http://fiji.sc/#download). Installation and use instructions available at http://imagej.net/FunImageJ.Contactkharrington@uidaho.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6031065 | biostudies-literature
Project description:No gold standard exists in the world of scientific image acquisition; a proliferation of instruments each with its own proprietary data format has made out-of-the-box sharing of that data nearly impossible. In the field of light microscopy, the Bio-Formats library was designed to translate such proprietary data formats to a common, open-source schema, enabling sharing and reproduction of scientific results. While Bio-Formats has proved successful for microscopy images, the greater scientific community was lacking a domain-independent framework for format translation.SCIFIO (SCientific Image Format Input and Output) is presented as a freely available, open-source library unifying the mechanisms of reading and writing image data. The core of SCIFIO is its modular definition of formats, the design of which clearly outlines the components of image I/O to encourage extensibility, facilitated by the dynamic discovery of the SciJava plugin framework. SCIFIO is structured to support coexistence of multiple domain-specific open exchange formats, such as Bio-Formats' OME-TIFF, within a unified environment.SCIFIO is a freely available software library developed to standardize the process of reading and writing scientific image formats.
| S-EPMC5142403 | biostudies-other
Project description:Most imaging studies in the biological sciences rely on analyses that are relatively simple. However, manual repetition of analysis tasks across multiple regions in many images can complicate even the simplest analysis, making record keeping difficult, increasing the potential for error, and limiting reproducibility. While fully automated solutions are necessary for very large data sets, they are sometimes impractical for the small- and medium-sized data sets common in biology. Here we present the Slide Set plugin for ImageJ, which provides a framework for reproducible image analysis and batch processing. Slide Set organizes data into tables, associating image files with regions of interest and other relevant information. Analysis commands are automatically repeated over each image in the data set, and multiple commands can be chained together for more complex analysis tasks. All analysis parameters are saved, ensuring transparency and reproducibility. Slide Set includes a variety of built-in analysis commands and can be easily extended to automate other ImageJ plugins, reducing the manual repetition of image analysis without the set-up effort or programming expertise required for a fully automated solution.
| S-EPMC4643401 | biostudies-literature
Project description:This work presents a MATLAB-based software package for high-throughput microscopy image analysis development, making such development more accessible for a large user community. The toolbox provides a GUI and a number of analysis workflows, and can serve as a general framework designed to allow for easy extension. For a new application, only a minor part of the object-oriented code needs to be replaced by new components, making development efficient. This makes it possible to quickly develop solutions for analysis not available in existing tools. We show its use in making a tool for quantifying intracellular transport of internalized peptide-drug conjugates. The code is freely available as open source on GitHub (https://github.com/amcorrigan/ia-lab).
| S-EPMC6675096 | biostudies-literature
Project description:Currently, in the state of Colorado and all other states within the United States of America with legalized marijuana programs, testing is required for bacteria, yeast, and mold on marijuana products. The Code of Colorado Regulations, 1 CCR 212-1, considers a passing result when a 1?g sample contains <104 colony forming units (CFU) for the total yeast and mold count (TYMC). These measurements are usually obtained by manually counting colonies on petri-dishes or 3?M™ Petrifilms™, which is a time consuming and user subjective process. Therefore, an automated counting method utilizing ImageJ has been developed for CFU analysis of TYMC on Petrifilms. The performance of this colony counting method was demonstrated by comparing manual and automated counts from marijuana flower samples containing spikes of Candida albicans as well as samples that tested positive for the presence of yeast and mold. Fifteen images of Petrifilms showing various concentrations of colonies were studied by fifteen users at two institutions using both the automated and manual counting methods. All counts from the automated ImageJ procedure were within 12% of those obtained manually. In twelve out of fifteen Petrifilms, the average count of the automated method was statistically similar to the manual counts. The statistical differences of the other three samples were observed to be random and caused by user errors. The automated counting method could be used to quickly count numbers that are as high as 400 CFUs, reducing time of analysis with improved documentation because the images and the electronic colony counts can be saved on a computer or cloud for long term storage and data access.
| S-EPMC6746339 | biostudies-literature
Project description:In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.
| S-EPMC4908129 | biostudies-literature
Project description:BackgroundStructured illumination microscopy has been extensively used in biological imaging due to its low cost and easy implementation. However, the lack of quantitative imaging capability limits its application in absolute irradiance measurements.MethodWe have developed a quantitative structured illumination microscopy image processing algorithm (QSIM) as a plugin for the widely used ImageJ software. QSIM can work with the raw images acquired by a traditional structured illumination microscope and can quantitatively measure photon numbers, with noise estimates for both wide-field images and sectioned images.Results and conclusionWe demonstrated the quantitative image processing capability of QSIM by imaging a mouse kidney section in 3D. The results show that QSIM can transform structured illumination microscopy from qualitative to quantitative, which is essential for demanding fluorescence imaging applications.
| S-EPMC4360942 | biostudies-literature
Project description:Quantifying fiber diameter is important for characterizing electrospun polymer scaffolds. Many researchers use manual measurement methods, which can be time-consuming and variable. Semi-automated tools exist, but there is room for improvement. The current work used Matlab to develop an image analysis program to quickly and consistently measure fiber diameter in scanning electron micrographs. The new Matlab method, termed "SIMPoly" (Semiautomated Image Measurements of Polymers) was validated by using synthetic images with known fiber size and was found to be accurate. The Matlab method was also applied by three different researchers to scanning electron microscopy (SEM) images of electrospun poly(lactic-co-glycolic acid) (PLGA). Results were compared with the semi-automated DiameterJ method and a manual ImageJ measurement approach, and it was found that the Matlab-based SIMPoly method provided measurements in the expected range and with the least variability between researchers. In conclusion, this work provides and describes SIMPoly, a Matlab-based image analysis method that can simply and accurately measure polymer fiber diameters in SEM images with minimal variation between users.
| S-EPMC7768983 | biostudies-literature