Tag: Image Analysis

Dapple 0.88pre4 – DNA Microarrays Image Analysis

Dapple 0.88pre4

:: DESCRIPTION

Dapple is a program for quantitating spots on a two-color DNA microarray image. Given a pair of images from a comparative hybridization, Dapple finds the individual spots on the image, evaluates their qualities, and quantifies their total fluorescent intensities.

Dapple is designed to work with microarrays on glass. The spot-finding techniques used are robust to uneven spot sizes and positional deviations caused by “wobbling” of the arraying robot, as well as image noise and artifacts. As long as your spots are consistently circular, Dapple has a good chance of finding them accurately.

::DEVELOPER

Jeremy Buhler

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

Dapple

:: MORE INFORMATION

Citation:

J. Buhler, T. Ideker, D. Haynor, “Dapple: Improved Techniques for Finding Spots on DNA Microarrays”, University of Washington Department of Computer Science & Engineering Technical Report UW-CSE-2000-08-05, (2000)  Supplement.

Spatter 0.5.3 – Image Analysis on cDNA Microarrays

Spatter 0.5.3

:: DESCRIPTION

Spatter is a software package which performs DNA microarray image analysis. It automatically quantitates two-color microarray images. It performs gridding, segmentation, and data extraction in an efficient and fully-automated manner, and does not require any user interaction while it runs.

::DEVELOPER

Alan Grosskurth

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/MacOsX
  • Python
  • C++ Compiler

:: DOWNLOAD

 Spatter

:: MORE INFORMATION

HTPheno – Image Analysis Pipeline for High-Throughput Phenotyping

HTPheno

:: DESCRIPTION

HTPheno is a software for high-throughput plant phenotyping is presented. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening.

::DEVELOPER

HTPheno Team

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

 HTPheno

:: MORE INFORMATION

Citation

A. Hartmann, T. Czauderna, R. Hoffmann, N. Stein and F. Schreiber.
HTPheno: An Image Analysis Pipeline for High-Throughput Plant Phenotyping.
BMC Bioinformatics, 12:e148, 2011.

SignalViewer – Image Analysis of cDNA Microarrays

SignalViewer

:: DESCRIPTION

SignalViewer is a Software Application for Image Analysis of cDNA Microarrays

::DEVELOPER

Fred Hutchinson Cancer Research Center

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux

:: DOWNLOAD

 SignalViewer

:: MORE INFORMATION

Citation

Laws RJ, Bergemann TL, Quiaoit F, Zhao LP.
SignalViewer: analyzing microarray images.
Bioinformatics. 2003 Sep 1;19(13):1716-7.

DCellIQ 1.0 – Quantitative Time-lapse Nuclei Image Analysis

DCellIQ 1.0

:: DESCRIPTION

DCellIQ ( Dynamic Cell Image Quantitator ) is a user-friendly software package that is designed for quantitative time-lapse nuclei image analysis for cell cycle studies, whose key features include automated nuclei detection, segmentation, quantification, tracking, cell cycle phase identification, and statistical analysis of the cell cycle duration.

::DEVELOPER

TMHRI Center for Bioengineering and Informatics

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • MATLAB

:: DOWNLOAD

DCellIQ

:: MORE INFORMATION

NeuriteIQ 1.0 – Quantitative Neurite Image Analysis

NeuriteIQ 1.0

:: DESCRIPTION

NeuriteIQ ( Neurite Image Quantitator ) is a user-friendly software package that is designed for quantitative Neurite image analysis, whose key features include automated neurite labeling and quantification of their length and brightness.

::DEVELOPER

TMHRI Center for Bioengineering and Informatics

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • MATLAB

:: DOWNLOAD

NeuriteIQ

:: MORE INFORMATION

Citation:

Y. Zhang, X. Zhou, A. Degterev, M. Lipinski, D. Adjeroh, J. Yuan, and S.T. Wong,
Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays,
NeuroImage, Vol. 35, issue 1, pp. 1502-15, Sept. 2007

NeuronIQ 1.5 – Quantitative Neuron Image Analysis

NeuronIQ 1.5

:: DESCRIPTION

NeuronIQ ( Neuron Image Quantitator ) is a user-friendly software package that is designed for quantitative Neuron image analysis, whose key features include automated dendrite and spine detection, quantification, and spine phenotype classification.

::DEVELOPER

TMHRI Center for Bioengineering and Informatics

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • MATLAB

    :: DOWNLOAD

    NeuronIQ

    :: MORE INFORMATION

    Citation:

    J. Cheng, X. Zhou, E. Miller, R.M. Witt, J. Zhu, B.L.Sabatini and S.T. Wong,
    A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy,
    Journal of Neuroscience Method, 165(1):122-134, Sept. 2007

    ZFIQ 1.0 – Quantitative Zebrafish Image Analysis

    ZFIQ 1.0

    :: DESCRIPTION

    ZFIQ ( Zebrafish Image Quantitator ) is a user-friendly software package that is designed for quantitative Zebrafish image analysis, whose key features include automated cell detection, segmentation, quantification, and phenotype classification.

    ::DEVELOPER

    TMHRI Center for Bioengineering and Informatics

    :: SCREENSHOTS

    :: REQUIREMENTS

    • Windows

    :: DOWNLOAD

    ZFIQ

    :: MORE INFORMATION

    Citation:

    Tianming Liu.
    A quantitative zebrafish phenotyping tool for developmental biology and disease modeling.
    IEEE Signal and Processing Magazine. 24(1):126-129. 2007

    CellC 1.2 – Quantification of Labeled Bacteria by Automated Image Analysis

    CellC 1.2

    :: DESCRIPTION

    CellC (Cell Counting) was developed and validated for quantification of bacterial cells from digital microscope images. CellC enables automated enumeration of bacterial cells, comparison of total count and specific count images [e.g., 4′,6-diamino-2-phenylindole (DAPI) and fluorescence in situ hybridization (FISH) images], and provides quantitative estimates of cell morphology. The software includes an intuitive graphical user interface that enables easy usage as well as sequential analysis of multiple images without user intervention. Validation of enumeration reveals correlation to be better than 0.98 when total bacterial counts by CellC are compared with manual enumeration, with all validated image types.

    ::DEVELOPER

    CellC Team

    :: SCREENSHOTS

    :: REQUIREMENTS

    :: DOWNLOAD

    CellC

    :: MORE INFORMATION

    Citation:

    Jyrki Selinummi, Jenni Seppälä, Olli Yli-Harja, and Jaakko A. Puhakka,
    Software for quantification of labeled bacteria from digital microscope images by automated image analysis
    BioTechniques, Volume 39, Number 6: pp 859-863.

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