Archive for the 'imageJ' Category

ImageJ plugin for navigation in very large images

Francis Géroudet Semester Project
Microengineering section, EPFL June 2008


In the biomedical field, specialists need to work on series of images taken under a microscope, covering an area interesting to analyze. The purpose of this project was, starting from a lot of images forming a mosaic, to build an intuitive navigation system, which could maintain the quality of basic images. We developed two plug-in for images: the first helps to prepare images, while the second deals with the navigation in the mosaic of images. The overall strategy used here was to cut basic images into small blocks at different zoom levels. Then, when displaying, the second plug- in is able to retrieve the blocks corresponding to the area considered by the user. Note that zooms are made using B-Splines.

Fig. 1: User interface for the plug- in preparation
Fig. 2: User interface for the navigation in the mosaic
Fig. 3: Example of use plugin navigation

The algorithms below are ready to be downloaded. Biomedical Imaging Group. EPFL

Available Algorithms

The algorithms below are ready to be downloaded. They are generally written in JAVA or in ANSI-C, either by students or by the members of the Biomedical Imaging Group.Please contact the author of the algorithms if you have a specific question.
JAVA: Plug-ins for ImageJ
JAVA classes are usually meant to be integrated into the public-domain software ImageJ.
bullet Drop Shape Analysis. New method based on B-spline snakes (active contours) for measuring high-accuracy contact angles of sessile drops.
bullet Extended Depth of Focus. The extended depth of focus is a image-processing method to obtain in focus microscopic images of 3D objects and organisms. We freely provide a software as a plugin of ImageJ to produce this in-focus image and the corresponding height map of z-stack images.
bullet Fractional spline wavelet transform. This JAVA package computes the fractional spline wavelet transform of a signal or an image and its inverse.
bullet Image Differentials. This JAVA class for ImageJ implements 6 operations based on the spatial differentiation of an image. It computes the pixel-wise gradient, Laplacian, and Hessian. The class exports public methods for horizontal and vertical gradient and Hessian operations (for those programmers who wish to use them in their own code).
bullet MosaicJ. This JAVA class for ImageJ performs the assembly of a mosaic of overlapping individual images, or tiles. It provides a semi-automated solution where the initial rough positioning of the tiles must be performed by the user, and where the final delicate adjustments are performed by the plugin.
bullet NeuronJ. This Java class for ImageJ was developed to facilitate the tracing and quantification of neurites in two-dimensional (2D) fluorescence microscopy images. The tracing is done interactively based on the specification of end points; the optimal path is determined on the fly from the optimization of a cost function using Dijkstra’s shortest-path algorithm. The procedure also takes advantage of an improved ridge detector implemented by means of a steerable filterbank.
bullet PixFRET. The ImageJ plug-in PixFRET allows to visualize the FRET between two partners in a cell or in a cell population by computing pixel by pixel the images of a sample acquired in three channels.
bullet Point Picker. This JAVA class for ImageJ allows the user to pick some points in an image and to save the list of pixel coordinates as a text file. It is also possible to read back the text file so as to restore the display of the coordinates.
bullet Resize. This ImageJ plugin changes the size of an image to any dimension using either interpolation, or least-squares approximation.
bullet SheppLogan. The purpose of this ImageJ plugin is to generate sampled versions of the Shepp-Logan phantom. Their size can be tuned.
bullet Snakuscule. The purpose of this ImageJ plugin is to detect circular bright blobs in images and to quantify them. It allows one to keep record of their location and size.
bullet SpotTracker Single particle tracking over noisy images sequence. SpotTracker is a robust and fast computational procedure for tracking fluorescent markers in time-lapse microscopy. The algorithm is optimized for finding the time-trajectory of single particles in very noisy image sequences. The optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure.
bullet StackReg. This JAVA class for ImageJ performs the recursive registration (alignment) of a stack of images, so that each slice acts as template for the next one. This plugin requires that TurboReg is installed.
bullet Steerable feature detectors. This ImageJ plugin implements a series of optimized contour and ridge detectors. The filters are steerable and are based on the optimization of a Canny-like criterion. They have a better orientation selectivity than the classical gradient or Hessian-based detectors.
bullet TurboReg. This JAVA class for ImageJ performs the registration (alignment) of two images. The registration criterion is least-squares. The geometric deformation model can be translational, conformal, affine, and bilinear.
bullet UnwarpJ. This JAVA class for ImageJ performs the elastic registration (alignment) of two images. The registration criterion includes a vector-spline regularization term to constrain the deformation to be physically realistic. The deformation model is made of cubic splines, which ensures smoothness and versatility.
Most often, the ANSI-C pieces of code are not a complete program, but rather an element in a library of routines.
bullet Affine transformation. This ANSI-C routine performs an affine transformation on an image or a volume. It proceeds by resampling a continuous spline model.
bullet Registration. This ANSI-C routine performs the registration (alignment) of two images or two volumes. The criterion is least-squares. The geometric deformation model can be translational, rotational, and affine.
bullet Shifted linear interpolation. This ANSI-C program illustrates how to perform shifted linear interpolation.
bullet Spline interpolation. This ANSI-C program illustrates how to perform spline interpolation, including the computation of the so-called spline coefficients.
bullet Spline pyramids. This software package implements the basic REDUCE and EXPAND operators for the reduction and enlargement of signals and images by factors of two based on polynomial spline representation of the signal.
bullet E-splines. A Mathematica package is made available for the symbolic computation of exponential spline related quantities: B-splines, Gram sequence, Green function, and localization filter.
bullet Fractional spline wavelet transform. A MATLAB package is available for computing the fractional spline wavelet transform of a signal or an image and its inverse.
bullet Fractional spline and fractals. A MATLAB package is available for computing the fractional smoothing spline estimator of a signal and for generating fBms (fractional Brownian motion). This spline estimator provides the minimum mean squares error reconstruction of a fBm (or 1/f-type signal) corrupted by additive noise.
bullet Hex-splines : a novel spline family for hexagonal lattices. A Maple 7.0 worksheet is available for obtaining the analytical formula of any hex-spline (any order, regular, non-regular, derivatives, and so on).
bullet MLTL deconvolution : This Matlab package implements the MultiLevel Thresholded Landweber (MLTL) algorithm, an accelerated version of the TL algorithm that was specifically developped for deconvolution problems with a wavelet-domain regularization.
bullet OWT SURE-LET Denoising : This Matlab package implements the interscale orthonormal wavelet thresholding algorithm based on the SURE-LET (Stein’s Unbiased Risk Estimate/Linear Expansion of Thresholds) principle.
bullet WSPM : Wavelet-based statistical parametric mapping, a toolbox for SPM that incorporates powerful wavelet processing and spatial domain statistical testing for the analysis of fMRI data.

pour commencer un article PLoS_one__guidelines-figure-table

Guidelines for Figure and Table Preparation


  1. Introduction
  2. Creative Commons Attribution License
  3. Titles and Legends
  4. General Considerations
  5. Figure Preparation
  6. Figure Dimensions
  7. Figure Types
  8. Uploading Figures to the PLoS Manuscript Submission System
  9. Multimedia Files
  10. Image Manipulation
  11. How To
  12. Format Tables
  13. Getting Help

1. Introduction

As part of the process of making scientific and medical literature openly accessible on the Web, PLoS uses a streamlined production process that takes authors’ submitted figures straight to the formatting stage. Most importantly, PLoS does not redraw figures submitted for publication in articles. Therefore, figure preparation is the author’s responsibility.

Please read the following guidelines carefully and thoroughly. Failure to comply with these guidelines may result in lower-quality figures and prolonged publishing time of your article.

2. Creative Commons Attribution License

All figures and photographic images will be published under a Creative Commons Attribution License (CCAL), which allows them to be freely used, distributed, and built upon as long as proper attribution is given. Please do not submit any figures or photos that have been previously copyrighted unless you have express written permission from the copyright holder to publish under the CCAL license.

For license inquiries, email

3. Titles and Legends

Titles and legends (captions) for figures published with articles (i.e., not Supporting Figures) should be included in the main manuscript text file, not as part of the figure files themselves. For each figure, list the following information at the end of the manuscript text, after the references:

  • Figure number (in sequence, using Arabic numerals: Figure 1, Figure 2, Figure 3, etc.)
  • Short title using a maximum of 15 words. The figure title should be bold type, using sentence case ending with a period (.). For example: Figure 1. Adaptation and its potential costs.
  • A detailed legend of 300 words maximum can follow the figure title. Figure parts should be indicated (see Parts Labels, below).
  • For more detailed information on Legends, see Author Guidelines: Figure Legends.

Supporting Figures. If Supporting Information figures will publish with your paper, please include the captions in the article file for PLoS Biology or Medicine, and in the File Title field of the online submission system for PLoS ONE, Neglected Tropical Diseases, Genetics, Computational Biology, or Pathogens.

Note: If at any point you have to change the numbering order of your figures, you must make sure that all figure captions correctly correspond with the figures.

4. General Considerations

There are two broad categories of figures in PLoS articles: (1) those publishing directly with the article and (2) Supporting Information figures.

Supporting Figures are not published directly in the article; rather, a hyperlink to the figure is provided in the online version of the published article. Figures publishing as Supporting Information can be in any file format or dimension, as long as they are no larger than 10 MB.

Provide a separate file for every figure in your manuscript, including Supporting Information figures. Figures should not be embedded in the main manuscript file. For example, if your manuscript has 10 figures, you would upload 10 individual files.

Note: PLoS converts EPS figures to TIFF before publishing so that they can be viewed in our online and PDF formats.

Recommended Graphics Software

Several graphics software packages are available to help you create high-quality graphics:

  • Adobe Photoshop
  • Adobe Illustrator
  • PowerPoint
  • CorelDraw
  • GIMP (freely distributed at

Note: Microsoft Word
PLoS does not recommend using Microsoft Word to adjust image size. Microsoft Word automatically down-samples figures and embeds them in the document at 72 dpi, so the images may be at a lower resolution and quality than is acceptable. We require that figures be created at a minimum resolution of 300 dpi.

Note: Microsoft Excel
PLoS does not recommend Excel to make or adjust figures. It does not have the optimal formatting to display graphics and images properly. This program should be used for tables only. See Table Guidelines for more information on formatting tables.

5. Figure Preparation

File Size

Individual figure files should not exceed 10 MB. If you are having trouble reducing the size of your files, refer to the section below titled Reduce TIFF File Size with LZW Compression.

Figure Quality

A figure that looks good on screen may not be at optimal resolution. Test your figures by sizing them to their intended dimensions and then printing them on your personal printer. The online version should look relatively similar to the personal-printer copy: it should not look fuzzy, jagged, pixilated, or grainy at intended print size.

Note: The quality of your figures will be only as good as the lowest-resolution element placed in them. In other words, if you created a 72 dpi line graph and save it as a 300 dpi TIFF, the image will still print out as a 72 dpi image.

Figure Format

Figures for publication must be submitted in high-resolution TIFF or EPS format only. Some figure types should be submitted in TIFF only (see Figure Types below). If you submit an EPS file it will be converted to TIFF prior to publishing. See How To: Convert Other File Types to TIFF below for more information on converting figure files to TIFF.

Color Mode

Figures containing color should be saved in RGB rather than CMYK or any other channels.

Layered TIFFs

TIFF files with multiple layers are not an accepted format for figures. Please make sure you provide us with a flattened version of your file. To flatten a layered TIFF file, open your figure in Photoshop. From the menu bar select Layer/Flatten Image and save the file. See also Combination Figures, below.

layered1 layered2
Figure example that has layers. Figure example that has the layers flattened. Only the Background layer remains.

Background Color

Create your figures using a white background. If you create figures using a transparent background, the figures may not display well in the online format.

Figure example showing a figure created with a transparent background. Transparent backgrounds do not work well in the online format. Figure example showing a figure created with a white background. White backgrounds display well in any format.

Lines, Rules, and Strokes

Lines should be at least 0.5 point and no more than 1.5 points in order to reproduce well in a PDF file or web format.

Figure example showing lines that are too thick and lines that are too light in color. Light color do not display well when published. Figure example showing the correct line widths and darker colored accent lines.

White Space

Each figure should be closely cropped to minimize the amount of white space surrounding it. PLoS recommends a 2 point white space border around each figure. Cropping figures improves accuracy when the figure is placed among other elements during production of the final published article.

Figure example that has too much white space. Figure example that has the correct amount of white space.

Text within Figures


Figure text must be in Arial font, between 8 and 12 points. Make sure that the visual information is readable at the size you select.

Figure text that requires a font family other than Arial (math symbols, etc.) must have the font information embedded in the figure file. See Embed Fonts in EPS Files and Convert Text to Outlines below for more information.

Parts labels

Multi-panel figures (those with parts A, B, C, and D) should be submitted as a single file that contains all parts of the figure. Label the figure itself with capital letters, Arial bold font, 12 points. Do not use punctuation (no periods or brackets). Any TIFFs with layers must be flattened (see Combination Figures below.)

Figure example that has the incorrect label format. Figure example that has the correct label format.
Figure example showing the use of the incorrect font family. Figure example correctly using the Arial font family.

6. Figure Dimensions

Figures for publication will be sized to fit 1, 1.5, or 2 columns of the final printable PDF of the article. Dimensions will also depend on the article type. Please follow the sizing recommendations below for your original submission to create high-quality, appropriately sized figures. See Figure Types below for descriptions and recommendations for line drawings, grayscale drawings, halftones, and combination figures.

Note: Figures for article types other than Research Articles are not sized or scaled. You must create figures for these articles types in their actual print or online display size. See below for sizing information.

Figure Alignment

Figures will be left-aligned on the page or column, so please design them accordingly.

Figure Width

Figures can have a width between 8.25 cm and 17.15 cm and a maximum height of 23.5 cm. If your figures have labels that are in 8 point type or if your figures are very detailed, it is recommended that your figure be created so that it will span two columns.

Article Type

  • 2-column: Research Article, Expert Commentary, Guidelines and Guidance, Learning Forum, Neglected Diseases, PLoS Medicine Debate, Primer, Review, Symposium.
  • 3-column: Editorial, Education, Essay, Health In Action, Historical and Philosophical Perspectives, Historical Profiles and Perspectives, Interview, Message from ISCB, Opinion, Perspective, Policy Forum, Policy Platform, Research In Translation, Special Report, Viewpoint.

Quick Reference – Figure Dimensions for 2-Column Article Types
Inches Pixels Centimeters Picas
Width for 1-Column Figures 3.25 in 312 px 8.25 cm 19.49 picas
Width for 1.5-Column Figures 4.75 – 5.0 in 456 – 480 px 12.06 – 12.7 cm 28.5 – 30 picas
Width for 2-Column Figures 6.75 in 648 px 17.15 cm 40.5 picas
Height Maximum for All Figures 9.25 in 888 px 23.5 cm 55.5 picas
Quick Reference – Figure Dimensions for 3-Column Article Types
Inches Pixels Centimeters Picas
Width for 1 Column Figures 2.15 in 207 px 5.5 cm 12.95 picas
Width for 2 Column Figures 4.5 in 434 px 11.5 cm 27.15 picas
Width for 3 Column Figures 6.75 in 648 px 17.15 cm 40.5 picas

7. Figure Types

Line Art

Line art has sharp, clean lines and geometrical shapes against a white background. Line art is typically used for tables, charts, graphs, and gene sequences. You can use a program like Illustrator to create high-quality line art. A minimum resolution of 300 dpi will maintain the crisp edges of the lines and shapes.

  • Format: EPS or TIFF
  • Minimum Resolution: 300 dpi


Grayscale figures contain varying tones of black and white. They contain no color, so grayscale is synonymous with « black and white. » The gray scale is divided into 256 sections with black at 0 and white at 255. Software for preparation of grayscale art includes Photoshop.

  • Format: EPS or TIFF
  • Minimum Resolution: 300 dpi


The best example of a halftone is a photograph, but halftones include any image that uses continuous shading or blending of colors or grays, such as gels, stains, microarrays, brain scans, and molecular structures. To prepare and manipulate halftone images, use Photoshop or a comparable photo-editing program.

  • Format: TIFF
  • Minimum Resolution: 300 dpi

Combination Figures

Combination figures contain two or more types of images, for example, a halftone figure containing text. You should embed the images, group the objects, or flatten the layers, and flatten transparencies before saving as TIFF at a minimum of 300 dpi.

  • Format: TIFF
  • Minimum Resolution: 300 dpi


Stereograms are figures with two almost identical pictures placed side by side which, when viewed through special glasses or a stereoscope, produce a three-dimensional image.

If you plan on submitting a stereogram as one of your figures, make sure this is clearly mentioned in the caption for the figure within the manuscript. Stereograms must be sized so that the centers of each of these images are 63 mm apart. Make sure that the stereogram figure is at the size you would like them to display. They will be checked prior to publishing, but this step will ensure your stereogram will be viewed properly.

Quick Reference Table for Common Figure Types
Line Art Grayscale Halftones Combination Figures
Required File Types EPS or TIFF EPS or TIFF TIFF TIFF
Required Resolution 300 dpi 300 dpi 300 dpi 300 dpi
Example Software for Preparation Adobe Illustrator Adobe Photoshop; GIMP Adobe Photoshop; GIMP Adobe Photoshop; GIMP

8. Uploading Figures to the PLoS Manuscript Submission System

Upload Order

  • Upload cover letter, then article file first. Ensure that it contains the figure legends, but not the figures themselves.
  • Figures should be numbered in the order they are first mentioned in the text, and uploaded in the same order. For example, Figure 1 should be uploaded as the first figure file, Figure 2 the second, etc.
  • Figures should be uploaded in the desired orientation.
  • Multimedia files (.avi or .swf files) must be uploaded as a Supporting Information file type and not a figure. See Multimedia Files below for more information.

Note: When a figure is uploaded to the PLoS manuscript submission system, a PDF file is created that contains the image but does not represent the final appearance of your figures in your published article. In addition, a « merged PDF » containing the article file and all of the figures is created automatically, which should be used by authors as a quick way to review their figures for egregious errors.

9. Multimedia Files

PLoS encourages authors to submit multimedia files that are crucial to the conclusions of the paper. Multimedia files should be smaller than 10 MB because of the difficulties that some users will experience in loading or downloading files. These files are published as Supporting Information. Preferred formats are:

  • Audio: MP3
  • Video: MOV, progressive download, 320 x 240 px frame size
  • Flash: SWF

10. Image Manipulation

Image files should not be manipulated or adjusted in any way that could lead to misinterpretation of the information present in the original image. Inappropriate manipulation includes but is not limited to:

  • The introduction, enhancement, movement, or removal of specific feature(s) within an image;
  • Unmarked grouping of images that should otherwise have been presented separately (for example, from different parts of the same gel, or from different gels, fields, or exposures);
  • Adjustments of brightness, contrast, or color balance that obscure, eliminate, or misrepresent any information.

Digital images in manuscripts nearing acceptance for publication may be scrutinized for any indication of improper manipulation. If evidence is found of inappropriate manipulation we reserve the right to ask for original data and, if that is not satisfactory, we may decide not to accept the manuscript.

We are grateful to staff at the Journal of Cell Biology (Rockefeller University Press) for their help in establishing these guidelines and procedures (

11. How To

Embed Fonts in EPS Files

Always embed fonts or create outlines when creating EPS files. If your figures require special symbols and Greek characters the text may not reproduce properly unless you embed your fonts or create outlines of the text. See the Convert Text to Outlines below for more information.

To embed fonts using Adobe Illustrator, open the EPS file. From the File Menu, select Save As. In the Save As dialog box, make sure that the Embed Fonts option is selected and click OK.

Convert Text to Outlines

When you convert text to outlines, the text is converted to a series of lines and fills. The reference to the font that was used to create the text is no longer present. This process makes it unnecessary for the PLoS production department to have the original font used to create the figure text. This is to ensure that your figures publish as you intended them to.

Example of text that has not been converted to outlines. Example of text that has been converted to outlines. Notice that every character is outlined.

You can use Adobe Illustrator to convert text to outlines by selecting the text you want to convert. Then from the Type menu, select Create Outlines (Shift + Control + O on PC, and Shift + Apple + O on Mac).

If you do not convert text to outlines, when your figure is opened during the production process any text in a non-standard font will automatically be substituted for default font. This can cause the text in the figure to render incorrectly.

Caution: You will not be able to change your text after it has been converted to outlines so make sure it is correct before converting.

Convert Other File Types to TIFF

Convert PDF to TIFF Using Photoshop

  1. Open the PDF file in Photoshop and select the page of the PDF that contains the figures to save as TIFF.
  2. From the File menu, select Save As to open the Save As dialog box.
  3. In the Save As dialog box, select TIFF from the Format dropdown list.
  4. When the TIFF Options dialog box displays, make sure to check the LZW compression checkbox.
  5. Click OK.

Convert EPS, JPG, GIF, or Other File Types to TIFF Using Photoshop

  1. Open the figure file in Photoshop.
  2. From the File menu, select Save As to open the Save As dialog box.
  3. In the Save As dialog box, select TIFF from the Format drop down list.
  4. When the TIFF Options dialog box displays, make sure to check the LZW compression checkbox.
  5. Click OK.

Note: Do not use the « optimize for web » wizard for any figures. Some programs may down sample your images to low resolution.

Convert PDF to TIFF Using Adobe Illustrator

  1. Open the PDF file in Adobe Illustrator, select the PDF page to export and click OK.
  2. From the File menu, select Export to display the Export dialog box.
  3. From the Export dialog box, select TIFF from the Save as Type drop down list and click OK.
  4. When the TIFF Options dialog displays, select LZW compression.
  5. Click OK to complete the process.

Convert EPS to TIFF Using Illustrator

  1. Open the EPS file in Adobe Illustrator.
  2. From the File menu, select Export to display the Export dialog box.
  3. From the Export dialog box, select TIFF from the Save as Type drop down list and click OK.
  4. When the TIFF Options dialog displays, select LZW compression.
  5. Click OK to complete the process.

Convert PowerPoint Files to High-Resolution TIFFs Using Adobe Acrobat and Photoshop

Caution: Do not use File > Save as > TIFF. This will result in a low-resolution, poor-quality figure.

Step I: Convert PowerPoint File to PDF

There are two possible ways to create PDFs from PowerPoint files: use the Adobe PDF menu in some versions of PowerPoint, or create a PDF via the Print command.

  1. Open your file in PowerPoint. From the Adobe PDF menu, select Change Conversion Settings. The PDFMaker Settings dialog displays.
  2. From the Conversion settings dropdown menu, select High Quality and click OK.
  3. From the Adobe PDF menu, select Convert to Adobe PDF. You will be asked to save the PDF file to a location of your choosing.
  4. Click OK.

– OR –

  1. Open your file in PowerPoint.
  2. Select Print from the File dropdown menu.
  3. Select the PDFCreator or similar tool in the Printer Name window.
  4. Click OK.

Note: If your PowerPoint file contains figures on multiple slides, after you create the PDF file you will need to use Adobe Acrobat to separate the figures/slides into individual files. You can also use PowerPoint to create separate files of each figure/slide.

Step II: Convert Multi-Page PDF File to Individual Files

  1. Using Adobe Acrobat Standard, open the PDF file that you created in Step 1. From the Document menu, select Pages and then Extract. The Extract Page dialog box displays.
  2. Enter the page numbers in the To and From fields and then select the Delete Pages checkbox. Checking this box will delete the page that you entered in the To and From fields from the PDF file.
  3. Click OK. The page that you specify in the previous step is now shown in Acrobat.
  4. From the File menu, select save and enter the file name (e.g., Figure 1) for the extracted page and then click OK.
  5. Repeat this process until a separate file is created for each figure/slide.

Step III: Convert Individual PDF Files to TIFFs

  1. Using Photoshop, open the PDF file that you created in Step II.
  2. From the File menu, select Save As.
  3. From the Save As dialog box, select TIFF from the Format dropdown list and click Save.
  4. In the TIFF Options dialog box, make sure the following options are selected. Under Image Compression, select LZW and under Pixel Order, select Interleaved.
  5. Click OK.
  6. Repeat this process until a separate TIFF file is created for each figure/slide.

Reduce TIFF File Size with LZW Compression

PLoS has a strict 10 MB figure file limit. To reduce the size of your figure, open your TIFF files in Photoshop. From the File menu, select Save As to open the Save As dialog box. In the Save As dialog box, select TIFF from the Format dropdown list. When the TIFF Options dialog box displays, make sure to check the LZW compression checkbox. Click OK.

Locate the Resolution Information in a TIFF File

You can locate the resolution of a figure file using Adobe Photoshop or through Windows Explorer.


To find the resolution of a figure using Photoshop, first open the file. Then from the Image menu, select Image Size. The Image Size dialog box will open displaying the figure dimensions, document size and resolution. You can decrease the size of a file, but you should not increase the resolution and/or dimensions of a file to meet the journals requirements. Increasing the file sizes manually may result in poor quality figures.

Windows Explorer

To check the resolution of a figure file using Windows Explorer, locate and select the file. Right-click and select Properties. In the Properties dialog box, select the Summary Tab. If you do not see the properties of the figures, click Advanced. This will display all of the properties associated with the selected figure. Look at the Horizontal Resolution and Vertical Resolution to determine the figure resolution.

12. Format Tables

Tables submitted for production should be included at the end of the article DOC or RTF file. For LaTeX submissions, table files should be uploaded individually into the online submission system. Tables that will be Supporting Information files can be submitted in any allowed format: Word, Excel, PDF, PPT, JPG, EPS, or TIFF.

Title and footnotes

Each table needs a concise title of no more than one sentence. The legend and footnotes should be placed below the table. Footnotes can be used to explain abbreviations.


Tables that do not conform to the following requirements may give unintended results when published. Problems may include movements of data (rows or columns), loss of spacing, or disorganization of headings. Note: Multi-part tables with varying numbers of columns or multiple footnote sections should be divided and renumbered as separate tables.

Table requirements:

  • Cell-based (e.g., created in Word with Tables tool or in Excel).
  • Editable (i.e., not graphic object).
  • Heading/subheading levels in separate columns.
  • Size no larger than one printed page (7 in x 9.5 in). Larger tables can be published as online supporting information.
  • No returns, tabs, or merged cells or rows.
  • No color, shading, lines, or rules.
  • No inserted text boxes or pictures.
  • No tables within tables.



Bad Use of Subhead Images Good Use of Subhead Images Example of incorrect subheads and use of text boxes Example of acceptable subheads within a table


13. Getting Help

Contact If you have questions about your figures after reading the guidelines, you can email

sourceforget and 3D; opensource; my list

name (alphab. order…) :






Fusion Viewer



GNU Triangulated Surface Library






Stani’s Python Editor




imageJ pour les débutants / for dummies

ImageJ est un logiciel de traitement d’image c’est-à-dire en fait pour un biologiste ou un médecin, d’analyse d’image pour faire ressortir la donnée biologique/médicale recherchée.

Le J indique que le programme a été écrit en un langage « Java » qui en fait un logiciel utilisable sur différents systèmes d’exploitation (mac, pc Windows…). C’est un logiciel multiplateformes, en raison de son fonctionnement sur une machine virtuelle Java.

ImageJ est un logiciel libre : le code source est en accès libres et peut être modifié.  Ses fonctions sont extensibles ; de nombreux plug-ins existent, qui abordent des domaines jusque là réservés aux logiciels commerciaux comme Aphelion : manipulation et visualisation d’images tridimensionelles, filtrages médians et morphologiques 3D, contours actifs (snakes), filtres diffusifs… Par ailleurs, il est possible de combiner les fonctions natives ou ajoutées en créant des macros – la maîtrise de Java n’est pas alors nécessaire.

La foisonnante diversité des plug-ins disponibles – plus d’une centaine – en fait son avantage principal, mais peut dérouter les néophytes : il est parfois difficile de trouver rapidement une fonction correspondant à un besoin précis ou on en trouve une dizaine pour un problème donné!

ImageJ peut être téléchargé gratuitement sur le site du NCBI.

les plugins:

Il se présente sous la forme d’une barre de menus flottante qui ouvre des fenêtres de données, elles aussi flottantes.

Barre des menus flottante de ImageJ

Barre des menus flottante de ImageJ

La plupart des opérations courantes de traitement d’images sont réalisables avec ImageJ : visualisation et ajustement de l’histogramme des niveaux de gris, débruitage, correction d’éclairage, détection de contours, seuillage, opérations entre images…

Des traitements issus de la morphologie mathématique sont aussi disponibles : érosion/dilatation, ligne de partage des eaux, squelettisation… En analyse d’images, ImageJ permet de dénombrer des particules, d’évaluer leurs ratios d’aspect, de mesurer diverses grandeurs (distances, surfaces), d’extraire des coordonnées de contours… L’ajout personnalisé de fonctions est possible grâce aux plugins  à écrire en java.

plug-in ImageJ Montpellier

MRI Cell Image Analyzer


tools.jpgImageJ est un programme d’analyse et de traitement de l’image gratuit et de domaine public. A partir de ImageJ, nous avons développé sur la plate-forme Montpellier RIO Imaging une interface visuelle pour le développement rapide d’applications pour l’analyse d’image. Cette interface complète ainsi les potentialités de ImageJ et permet, sur la base d’un drag and drop à partir d’une liste d’opérations existantes de créer facilement et rapidement des séquences d’opérations pour l’analyse interactive d’images ou l’automatisation de l’analyse d’un grand nombre d’images. Cette interface a été utilisée pour une grande variété d’applications. Cette interface et ces applications sont accessibles librement sous licence GNU (voir GNU General Public License).

MRI-CIA a été présenté pour la première fois à la communauté ImageJ à la « ImageJ User and Developer Conference 2006« . Si vous utilisez MRI-CIA, merci de citer l’article suivant :

Pour plus d’informations :


  • MRI Cell Image Analyzer – Automatic analysis of microscopy images (pdf | view online)
    Présentation de MRI-CIA en anglais, par Volker Bäcker, le 13.12.2005 au CRBM, Montpellier, France.

measure infections.jpgAteliers

    • Pour suivre l’atelier, il est nécessaire de télécharger ces images :

o vous trouverez ici des macros développées durant l’atelier.


  • Publications utilisant MRI Cell Image Analyzer
  • applications développées avec MRI-CIA
  • MRI Object Modeling Workbench
  • MRI-CIA sur le documentation wiki de ImageJ

Colocalization and confocal images and imageJ Matlab

The laser scanning confocal microscope (LSCM) generates images of multiple labelled fluorescent samples. Colocalization of fluorescent labels is frequently examined.

Colocalization is usually evaluated by visual inspection of signal overlap or by using commercially available software tools, but there are limited possibilities to automate the analysis of large amounts of data.



–Colocalization image processing imageJ

Colocalisation analysis is an subject plagued with errors and contention. The literature is full of different methods for colocalisation analysis which probably reflects the fact that one approach does not necessarily fit all circumstances.

Analysis can be considered qualitative or quantitative. However, opinions differ as to which category the different approaches fall!

Qualitative analysis can be thought of as « highlighting overlapping pixels ». Although this is often given as a number (« percentage overlap ») suggesting quantification, the qualitative aspect arises when the user has to define what is considered « overlapping ». The two channels have a threshold set and any areas where they overlap is considered « colocalised ». Qualitative analysis has the benefit of being readily understood with little expert knowledge but suffers from the intrinsic user bias of « setting the threshold ». There are algorithms available which will automate the thresholding without user intervention but these rely on analysis of the image’s histogram which is subject to user intervention during acquisition.

Quantitative analysis removes user bias by analysing all the pixels based on of their intensity (it must be noted that some authors consider this a draw back rather than an advantage due to the intrinsic uncertainty of pixel intensity; see Lachmanovich et al. (2003) J. Microscopy, 212, 122-131). There are a number of coefficients detailed in the literature which can be calculated using ImageJ; each coefficient has it’s strengths and weaknesses and should be thoroughly researched before being used. It is this requirement for the coefficient to be fully understood which is a disadvantage when trying to convey information to research peers who are experts in biology, and not necessarily mathematics.

One key issue that can confound colocalisation analysis is bleed through. Colocalisation typically involves determining how much the green and red colours overlap. Therefore it is essential that the green emitting dye does not contribute to the red signal (typically, red dyes do not emit green fluorescence but this needs to be experimentally verified). One possible way to avoid bleed-through is to acquire the red and green images sequentially, rather than simultaneously (as with normal dual channel confocal imaging) and the use of narrow band emission filters. Single and unlabelled controls must be used to assess bleed-through.

Intensity Correlation Analysis

This plugin generates Mander’s coefficients (see below) as well as performing Intensity Correlation Analysis as described by Li et al. To fully understand this analysis you should read:
Li, Qi, Lau, Anthony, Morris, Terence J., Guo, Lin, Fordyce, Christopher B., and Stanley, Elise F. (2004). A Syntaxin 1, G{alpha}o, and N-Type Calcium Channel Complex at a Presynaptic Nerve Terminal: Analysis by Quantitative Immunocolocalization. Journal of Neuroscience 24, 4070-4081.

It is bundled with WCIF ImageJ and can be downloaded alone here.


Manders’ Coefficient » (formerly Image Correlator plus<!–[if supportFields]> XE « Image Correlator plus: Wayne Rasband, Tony Collins«  <![endif]–> ” and “Red-Green Correlator” plugins)

This plugin generates various colocalisation coefficients for two 8 or 16-bit images or stacks.

The plugins generate a scatter plots plus correlation coefficients. In each scatter plot, the first (channel 1) image component is represented along the x-axis, the second image (channel 2) along the y-axis. The intensity of a given pixel in the first image is used as the x-coordinate of the scatter-plot point and the intensity of the corresponding pixel in the second image as the y-coordinate.

The intensities of each pixel in the “Correlation Plot” image represent the frequency of pixels that display those particular red/green values. Since most of you image will probably be background, the highest frequency of pixels will have low intensities so the brightest pixels in the scatter plot are in the bottom left hand corner – i.e. x~ zero, y ~ zero. The intensities in the “Red-Green correlation plot” image represent the actual colour of the pixels in the image.

Mito-DsRed; ER-EGFP

Pearson’s correlation (R)=0.34

Overlap coefficient (R)=0.40

Nred ÷ Ngreen pixels=0.66

Colocalisation coefficient for red (Mred)=0.96

Colocalisation coefficient for green (Mgreen)=0.49

TMRE (red) plus Mito-pericam (Green)

Pearson’s correlation Rr=0.93

Overlap coefficient R=0.94

Nred ÷ Ngreen pixels=0.93

Colocalisation coefficient (red) Mred=0.99

Colocalisation coefficient (green) Mgreen=0.98

Both plugins generate various colocalisation coefficients: Pearson’s (Rr), Overlap (R) and Colocalisation (M1, M2) See Manders, E.E.M., Verbeek, F.J. & Aten, J.A. ‘Measurement of co-localisation of objects in dual-colour confocal images’,  (1993) J. Microscopy, 169, 375-382. See tutorial sheet ‘Colocalisation’ for details. The threshold is also reported (0,0 means no threshold was used).

Colocalisation Test

When a coefficient is calculated for two images, it is often unclear quite what this means, in particular for intermediate values. This raises the following question: how does this value compare with what would be expected by chance alone?

There are several approaches that can be used to compare an observed coefficient with the coefficients of randomly generated images. Van Steensel (3) adopted an approach where the observed colocalisation between channel 1 and channel 2 was compared to colocalisation between channel 1 and a number of channel 2 images that had been translated (i.e. displaced by a number of pixels) in increments along the image’s X-axis. Fay et al (4) extended this approach by translating channel 2 in 5-pixel increments along the X- and Y-axis (i.e., –10, –5, 0, 5, and 10) and ± 1 slices in the Z-axis. This results in 74 randomisations (plus one original channel 2). The observed correlation was compared to these 74 and considered significant if it was greater than 95% of them.

Costes et al. (5) subsequently adopted a different approach, based on “scrambling” channel 2. The original channel 1 image was compared to 200 “scrambled” channel 2 images; the observed correlations between channel 1 and channel 2 were considered significant if they were greater than 95% of the correlations between channel 1 and scrambled channel 2s.

Costes’ scrambled images were generated by randomly rearranging blocks of the channel-2 image. The size of these blocks was chosen to equal the point spread function (PSF) of the image.

An approximation of Costes’ approach is used by Bitplane’s Imaris and also the Colocalisation Test plugin. For Imaris, a white noise image is smoothed with a Gaussian filter the width of the image’s PSF. The Colocalisation Test plugin generates a randomized image by taking random pixels from the channel-2 image; it then smoothes the image with a Gaussian filter, which is again the width of the image’s PSF.

The Colocalisation Test plugin calculates Pearson’s correlation coefficient for the two selected channels (Robs) and compares this to Pearson’s coefficients for channel 1 against a number of randomized channel-2 images (Rrand).

–Colocalization image processing matlab:


Automated high through-put colocalization analysis of multichannel confocal images

M. Kreft , I. Milisav , M. Potokar and R. Zorec

Lab. Neuroendocrinology-Molecular Cell Physiology, Inst. Pathophysiology, Medical Faculty, Zaloska 4, 1000 Ljubljana and Celica Biomed. Sciences Center, Stegne 21, 1000, Ljubljana, Slovenia

accepted 20 April 2003.

Available online 15 July 2003.

We developed a simple tool using Matlab to automate the colocalization procedure and to exclude the biased estimations resulting from visual inspections of images. The script in Matlab language code automatically imports confocal images and converts them into arrays. The contrast of all images is uniformly set by linearly reassigning the values of pixel intensities to use the full 8-bit range (0–255). Images are binarized on several threshold levels. The area above a certain threshold level is summed for each channel of the image and for colocalized regions. As a result, count of pixels above several threshold levels in any number of images is saved in an ASCII file. In addition Pearson’s r correlation coefficient is calculated for fluorescence intensities of both confocal channels. Using this approach quick quantitative analysis of colocalization of hundreds of images is possible. In addition, such automated procedure is not biased by the examiner’s subject visualization.



Blog Stats

  • 220 821 hits


Flickr Photos

avril 2019
« Oct