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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.
DISCO / Data Informed Segmentation of Cell Objects
Segments and tracks budding yeast cells. DISCO is a comprehensive framework structured into several stages for integrated identification, segmentation and tracking of cells: (i) identification of physical features of the microfluidic device, (ii) supervised classification to identify cell centers, (iii) segmentation using a morphologically constrained cell-shape model, (iv) incorporation of temporal information to refine cell center prediction and (v) iterative greedy optimization of cell contours.
Enables cell lineage tracking. MicrobeTracker utilizes cell shape and timelapse information to achieve cell outlining. It can track fluorescently labeled molecules in cell lineages over several generations or in difficult-to-resolve samples, such as densely-packed or filamentous cells, from time-lapse sequences. This tool is delivered with an accessory tool, called SpotFinder, that detects small round spots, generating precise cell coordinates of fluorescently labeled foci inside cells.
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.
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.
SMASH / Semi-automatic Muscle Analysis using Segmentation of Histology
Provides an automatic and standardized image segmentation platform. SMASH permits users to measure multiple facets of muscle histology. It carries a high monetary cost and is not specifically designed for skeletal muscle analysis. This tool generates results validated against legacy methods showing largely consistent results between methods for fiber type and centrally nucleated fiber (CNF) percentages. It reduces the border region between adjacent fibers, it is also capable of delineating interstitial space between adjacent fibers when there is an appreciable separation.
A simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode.
Consists of a classification plugin for ImageJ. ImageSURF uses standard bitmap formats for: (1) class annotations, (2) making the training process open, repeatable and (3) incorporating large training sets created by multiple users across multiple sessions with the software of their choice. This tool utilizes primitive data structures to avoid the substantial overheads of object data structures such as the WEKA Instance. It can be used for studying the aggregation and deposition of amyloid-β peptide in brain tissue of Alzheimer’s disease rodent models.
Amira 3D Software for Life Sciences
Allows users to visualize, manipulate, and understand data from imaging modalities such as computed tomography, microscopy or Magnetic resonance imaging (MRI). Amira 3D Software for Life Sciences provides features to import and process 2D and 3D images data, visualization techniques and tools for visual analysis. Users can also create and share presentations. The base product can be customized by adding functional extensions to fit special needs in different application areas.
Allows segmentation of fine structures. ABSnake is a contour model, available as an ImageJ plugin, that mixes two classical approaches of deformable models: deformable curves and classical active contours. The plugin can enter invaginations because points are attracted towards locations with high gradient magnitude along a direction perpendicular to the contour. It can reproduce the classical behavior of snake with high values of regularization, and can also segment very thin and complex structures such as ones present in biological images.
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