# Image lab

> Look at, measure and count microscope images the way you would in ImageJ or Fiji, in your browser.

Source: https://khushisingh.in/tools/image-lab/
Author: Khushi Singh (khushisingh.in). Licence: CC BY 4.0 for text and original illustrations, unless a credit says otherwise.
Evidence labels used on this site: established, strong evidence, emerging, hypothesis, debated. Where a page has Simple, Curious and Expert versions, all three follow.

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Tools

Look at, measure and count microscope images the way you would in ImageJ or Fiji, in your browser. Your images never leave your device, and every step is saved as an ImageJ macro you can re-run in Fiji.

Private by design

Your image is opened and analysed by your own browser. It is never uploaded or stored anywhere, and this page makes no outside requests (the two demo images come from this site). Even so, think before sharing results from unpublished or clinical images.

*[Here the web page has an interactive activity; open the source URL to use it.]*

## Getting started

1. **Open an image**, or press *Try a demo: cells* for a five-channel Cell Painting field of human U2OS cells. Several single-channel TIFFs of the same size open as the channels of one image.
2. **Adjust the display** in the *Display* panel: choose a channel, its colour and its minimum and maximum, or press *Auto*. This changes only how the image looks; the numbers you measure stay the same.
3. **Draw a region** with the rectangle (R), oval (O), polygon (G) or freehand (F) tool, then press *Measure* (M). Draw a line (L) for a profile. Drag with the move tool (H) or two fingers to pan; scroll or pinch to zoom.
4. **Count** by hand with the cell counter (P), or automatically under *Threshold and count*.
5. **Save** the results as CSV, the view as PNG with a scale bar, the image as a calibrated TIFF, and the *macro log*, which repeats the same steps in Fiji.

Keyboard: every tool has a key, the arrow keys pan, `+` and `−` zoom, `0` fits the image, and regions can also be entered by numbers in the *Regions* panel.

## What the numbers mean

- **Mean** is the average pixel value in the region; **IntDen** (integrated density) is area × mean, and **RawIntDen** the plain sum of pixel values, as in ImageJ.
- **CTCF** (corrected total cell fluorescence) = IntDen − area × the mean of a background region. Mark one region as *background* and every measurement gets a CTCF column. (evidence: established)
- **Circularity** = 4π × area ÷ perimeter²: 1 for a perfect circle, lower for elongated or ragged shapes. **Feret** is the longest distance across the shape.
- **Colocalization** has two kinds of answer. Pearson’s *r* asks whether the two signals rise and fall together; Manders’ M1 and M2 ask what fraction of each signal sits where the other is present, above a threshold (Costes’ method picks it automatically). Report both, with the thresholds, and compare with a control. (evidence: established)

## Measuring honestly

- **Measure raw images.** Use the original, unsaturated TIFF from the microscope. JPEG compression and screenshots change pixel values; the Image lab warns when a file is lossy. (evidence: established)
- **Adjust the whole image, and say so.** Brightness and contrast changes must apply to the whole image and be stated in the figure legend. Never erase, paste or move features. (evidence: established)
- **Treat images you compare the same way**: same exposure, same display range, same processing. The macro log is your record of what was done.
- **Filters change data.** Blurring, median filtering and background subtraction are fine for finding objects, but report them, and measure intensities on the unfiltered image where you can.

## What it does not do (use Fiji)

The Image lab covers the everyday 2D tasks. For proprietary formats (CZI, LIF, ND2 and others; save them as TIFF in Fiji or the microscope software first), 3D rendering, deconvolution, tracking, plugins and batch processing, use [Fiji](https://fiji.sc/). The background subtraction uses ImageJ’s “sliding paraboloid” option rather than the classic rolling ball, so values can differ slightly from Fiji’s default; the threshold methods and measurements follow ImageJ’s definitions.

Demo images: BBBC022v1 (Gustafsdottir et al. 2013; one untreated control field, plate 20585, well C11, site 2, all five channels unchanged) and BBBC039v1 (Caicedo et al. 2019; an 8-bit copy), from the Broad Bioimage Benchmark Collection (Ljosa et al. 2012), both CC0. Built with UTIF (MIT licence) for reading TIFF files.

## References

1. Schindelin J, et al. 2012. Fiji: an open-source platform for biological-image analysis. Nature Methods 9:676–682. [doi:10.1038/nmeth.2019](https://doi.org/10.1038/nmeth.2019)
2. Schneider CA, Rasband WS, Eliceiri KW. 2012. NIH Image to ImageJ: 25 years of image analysis. Nature Methods 9:671–675. [doi:10.1038/nmeth.2089](https://doi.org/10.1038/nmeth.2089)
3. Rossner M, Yamada KM. 2004. What’s in a picture? The temptation of image manipulation. Journal of Cell Biology 166:11–15. [doi:10.1083/jcb.200406019](https://doi.org/10.1083/jcb.200406019)
4. Cromey DW. 2010. Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images. Science and Engineering Ethics 16:639–667. [doi:10.1007/s11948-010-9201-y](https://doi.org/10.1007/s11948-010-9201-y)
5. Waters JC. 2009. Accuracy and precision in quantitative fluorescence microscopy. Journal of Cell Biology 185:1135–1148. [doi:10.1083/jcb.200903097](https://doi.org/10.1083/jcb.200903097)
6. Schmied C, et al. 2023. Community-developed checklists for publishing images and image analyses. Nature Methods 21:170–181. [doi:10.1038/s41592-023-01987-9](https://doi.org/10.1038/s41592-023-01987-9)
7. McCloy RA, et al. 2014. Partial inhibition of Cdk1 in G2 phase overrides the SAC and decouples mitotic events. Cell Cycle 13:1400–1412. (The usual citation for CTCF.) [doi:10.4161/cc.28401](https://doi.org/10.4161/cc.28401)
8. Sternberg SR. 1983. Biomedical image processing. Computer 16:22–34. (The rolling-ball background.) [doi:10.1109/MC.1983.1654163](https://doi.org/10.1109/MC.1983.1654163)
9. Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9:62–66. [doi:10.1109/TSMC.1979.4310076](https://doi.org/10.1109/TSMC.1979.4310076)
10. Manders EMM, Verbeek FJ, Aten JA. 1993. Measurement of co-localization of objects in dual-colour confocal images. Journal of Microscopy 169:375–382. [doi:10.1111/j.1365-2818.1993.tb03313.x](https://doi.org/10.1111/j.1365-2818.1993.tb03313.x)
11. Costes SV, et al. 2004. Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophysical Journal 86:3993–4003. [doi:10.1529/biophysj.103.038422](https://doi.org/10.1529/biophysj.103.038422)
12. Bolte S, Cordelières FP. 2006. A guided tour into subcellular colocalization analysis in light microscopy. Journal of Microscopy 224:213–232. [doi:10.1111/j.1365-2818.2006.01706.x](https://doi.org/10.1111/j.1365-2818.2006.01706.x)
13. Dunn KW, Kamocka MM, McDonald JH. 2011. A practical guide to evaluating colocalization in biological microscopy. American Journal of Physiology – Cell Physiology 300:C723–C742. [doi:10.1152/ajpcell.00462.2010](https://doi.org/10.1152/ajpcell.00462.2010)
14. Aaron JS, Taylor AB, Chew T-L. 2018. Image co-localization – co-occurrence versus correlation. Journal of Cell Science 131:jcs211847. [doi:10.1242/jcs.211847](https://doi.org/10.1242/jcs.211847)
15. Gustafsdottir SM, et al. 2013. Multiplex cytological profiling assay to measure diverse cellular states. PLoS ONE 8:e80999. (Image set BBBC022v1.) [doi:10.1371/journal.pone.0080999](https://doi.org/10.1371/journal.pone.0080999)
16. Caicedo JC, et al. 2019. Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. Cytometry Part A 95:952–965. (Image set BBBC039v1.) [doi:10.1002/cyto.a.23863](https://doi.org/10.1002/cyto.a.23863)
17. Ljosa V, Sokolnicki KL, Carpenter AE. 2012. Annotated high-throughput microscopy image sets for validation. Nature Methods 9:637. [doi:10.1038/nmeth.2083](https://doi.org/10.1038/nmeth.2083)
18. Jamali N, et al. 2021. 2020 BioImage Analysis Survey: community experiences and needs for the future. Biological Imaging 1:e4. [doi:10.1017/S2633903X21000039](https://doi.org/10.1017/S2633903X21000039)