Human colon tissue

Description
One of the principal challenges in counting or segmenting cells or cell nuclei is dealing with clustered objects such as in tissues. To help assess algorithms' performance in this regard, synthetic 3D image sets of human colon tissue are provided in two diferent levels of quality: high SNR and low SNR. Ground truth is available as well. The datasets are part of [Masaryk University Cell Image Collection (MUCIC)](http://cbia.fi.muni.cz/datasets/) as well [Broad Bioimage Benchmark Collection (BBBC)](https://data.broadinstitute.org/bbbc/) - entry BBBC027.
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ColorLab

Description
COLORLAB is a component for processing, representing and reproducing color in a MATLAB environment. Among others, some of the functionalities it makes able to: -Represent the color content of any image in chromatic diagrams and tristimulus spaces in any system of primaries. -Compute advanced color descriptions of any image using several color appearance models (CIELab, CIEluv, ATD, Rlab, LLab, SVF and CIECAM). An userguide is provided.
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Annotated two-photon images of dendritic spines

Description
A fully annotated dataset of Two-Photon Laser Scanning Microscopy (2PLSM) images of three types of dendritic spines. We make a standard dendritic analysis dataset publicly available including raw data, manual annotations, and manual labels. Manual labels and annotation (segmentations) are provided by a neuroscience expert. The dataset has been used in Ghani2016 to perform dendritic spine analysis.
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simpleElastix

Description

quote:

Elastix cite{Klein2010} is an open source, command-line program for intensity-based registration of medical images that allows the user to quickly configure, test, and compare different registration methods. SimpleElastix is an extension of SimpleITK cite{Lowekamp2013} that allows you to configure and run Elastix entirely in Python, Java, R, Octave, Ruby, Lua, Tcl and C# on Linux, Mac and Windows. The goal is to bring robust registration algorithms to a wider audience and make it easier to use elastix, e.g. for Java-based enterprise applications or rapid Python prototyping.

Python example

import SimpleITK as sitk
resultImage = sitk.Elastix(sitk.ReadImage("fixedImage.nii"), sitk.ReadImage("movingImage.nii"))

Local Phase Quantization (LPQ) descriptors

Description
This is a Matlab implementation of Local Phase Quantization (LPQ) texture descriptors that is robust to image blurring due to the use of phase information. Theoretical background could be found here: http://www.ee.oulu.fi/research/mvmp/mvg/files/pdf/ICISP08.pdf
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MyTardis

Description
MyTardis is free and open-source data management software. It facilitates annotation, sharing and archiving of data and metadata collected from different modalities. It focuses on integration with scientific instruments, instrument facilities and research storage and computing infrastructure; to address the challenges of data storage, data access, collaboration and data publication. It is currently being used to capture data from areas such as optical microscopy, electron microscopy, medical imaging, protein crystallography, neutron and X-ray scattering, flow cytometry, genomics and proteomics. ## Key features * Easy instrument integration. * Discipline specific: MX, Imaging, Microscopy, Genomics ... * Wide range of data formats & supported instruments. * Secure cloud data storage & access. * Simple data sharing. * Researcher controlled data publishing. * APIs for programmatic access to data and metadata.
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BSIF (Binarized Statistical Image Features)

Description
This is a software toolbox that extends the original BSIF code allowing the utilization of a GPU in Matlab to compute the features. It contains: -Matlab function to calculate BSIF in CPU -Matlab function extension to calculate BSIF in GPU -Pre-learnt filters -Usage instructions
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CBIRetrieval

Description
This is a Java content-based image retrieval software components. It can be runned independantly or connected to a Cytomine server. Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based" means that the search algorithm analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. The CBIRetrieval library is: Incremental: You can add new images all over the time. Scalable: Run as many server as you want. Client performs search on all servers. Flexible: Run as a simple app (command line) or use the JAR in your own JVM app/server (java import) Opensource/Free: Apache 2.0 CBIRetrieval is a java library for CBIR, CBIRest is a server with a REST HTTP API with CBIRetrieval embedded. If you want to connect a software/webapp with a CBIR engine, you should use CBIRest. This is a fast multi-threaded and noSQL implementation of the algorithm published in: Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies Marée, Raphaël; Denis, Philippe; Wehenkel, Louis; Geurts, Pierre,in ACM Proceedings MIR 2010 (2010, March). It was applied on histology images and radiology images.
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Digital Reconstructions of Neuronal Morphology: Three Decades of Research Trends

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BBBC003v1 MOUSE EMBRYOS

Description
Fluorescence microscopy cannot be used to image human embryos to determine embryo viability for in vitro fertilization because the introduction of exogenous fluorescent dyes is considered a toxic procedure. As a result, embryo viability has been measured primarily using differential interference contrast (DIC). A human can readily segment the embryo (and, to some extent, individual cells) in a DIC image, but automatic segmentation remains a challenge due to the cosine-dependent shading inherent in DIC images. There are 15 images. The images were acquired using a Nikon Eclipse TE200 microscope with a 20x, 0.45 NA objective lens and a 0.52 NA condenser lens, and are provided courtesy of the W.M. Keck 3D Fusion Microscope Facility at Northeastern University. Each image contains 640 x 480 pixels with an approximate size of 0.42 x 0.42 ?m.
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