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Analyzes, processes and visualizes multi-dimensional microscopy images. BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes.
A tool for segmentation, fluorescence quantification, and tracking of cells on microscopy images. CellX decodes the information across the cell membrane and guarantees optimal detection of the cell boundaries on a per-cell basis. Graph cuts account for the information of the cell boundaries through directional cross-correlations, and they automatically incorporate spatial constraints. The method accurately segments images of various cell types grown in dense cultures that are acquired with different microscopy techniques.
Provides general purpose functionality for reading, writing, processing and analysis of images. In the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization. It uses ImageMagick to read and save images, and supports more than 80 image formats, including JPEG, TIFF, TGA, GIF and PNG. EBImage also supports standard geometric transformations such as rotation, reflection, cropping, translation and resizing. Classical image processing tools are available: linear filtering, morphological erosion and dilation, fast distance map computation, contour delineation and area filling.
Proposes a large collection of generic tools based on mathematical morphology to process binary and grey-level 2D and 3D images, integrated into user-friendly plugins. The library provides different categories of functions, corresponding to standard image processing workflows: (i) image processing and filtering; (ii) segmentation; (iii) post-processing; (iv) quantitative analysis; (v) library re-usability. The cell-resolved data provided by MorphoLibJ will be useful for the analysis of cell lineage, and the modelling of plant growth and morphogenesis in 3D.
Segments compound images appearing in biomedical documents. FigSplit is a Connected Component Analysis (CCA)-based scheme for segmenting compound images, including stitched compound images, while addressing both over and under-segmentation issues. It provides a pre-processing step to broaden and un-blur gaps in images so that more images can be segmented. It then adds an assessment step to detect, evaluate and modify segmentation errors, and re-separate some of the images accordingly.
STAPLE / Simultaneous Truth And Performance Level Estimation
Provides estimates of performance parameters accounting for external standard reference. STAPLE is an algorithm for taking a collection of both binary and unordered multicategory segmentations. It simultaneously constructs an estimate of the hidden true segmentation and an estimate of the performance level of each segmentation generator. It can also be used to characterize any type of segmentation generator, including new segmentation algorithms or human operators, by direct comparison to the estimated true segmentation.
3D Tissue Organization Toolbox
Provides all the necessary methods for nuclei segmentation, cell identification and analysis of cell interaction in 3D. 3D Tissue Organization Toolbox is an ImageJ plugin that covers different aspects of spatial analysis of tissues in 3D, ranging from nuclei segmentation to analysis of different aspects of cellular interaction and network mapping. It was used to investigate current and novel aspects of the unique architecture of the pancreatic islet of Langerhans.
SIMA / Sequential IMage Analysis
Facilitates common analysis tasks related to fluorescence imaging. Functionality SIMA includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. A graphical user interface (GUI) has also been developed for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages.
An automated algorithm for 3D cell nuclei segmentation based on gradient flow tracking. The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy. To validate the efficacy and performance of the proposed segmentation algorithm, we evaluated it by using synthesized and real biological images. The results show that the algorithm is able to segment juxtaposed nuclei correctly, a persistent problem in the field of cellular image analysis.
An automated segmentation method that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation. First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.
FMAj / Fly Muscle Analysis in Java
Performs quantitative characterization of muscle phenotypes in time-series images. FMAj is composed of three modules: (i) the first one captures experimental metadata derived from the images or via manual annotation by the user; (ii) the second performs segmentation of muscle cells and nuclei in a semi-automated fashion.; (iii) the third module achieves comparative phenotypic analysis, such as comparing the cell morphology between control and genetically perturbed cells.
BCOMS / Biologically Constrained Optimization based cell Membrane Segmentation
Automates cell shape extraction in C. elegans embryos. BCOMS provides a user-friendly framework that computerizes not only the segmentation process but also the evaluation process. The performance of BCOMS was validated by comparisons with the ground truth and by comparing the results in two adjacent time points. This method is also applicable to other model organisms by customizing the biological constraints.
Evaluates and compares the performance levels of Cell image segmentation (CIS) algorithms with statistical confidence. Cell-Imaging uses significance values for differences in CIS algorithm performance in combination with other factors such as computational execution time. It can be used to evaluate the performance level of a CIS algorithm against a hypothesized value, and classify CIS algorithms into different classes in terms of performance accuracies based on the criteria of performance accuracies.
A MATLAB based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. CellSegm has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classification of cell candidates. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in MATLAB, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.
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