Avoiding Pitfalls During ImageAcquisition

Arnold Fertin1 Yves Usson1

1TIMC-IMAG, UMR 5525, Grenoble Alpes University

November

IntroductionCommon pitfalls

Conclusion

Digital image in microscopy: from light to pixelsImage quantizationImage histogram and contrast

Image sensor: going to digital

1 the continuous light distribution is spatially sampled byphotodetector array

2 temporal sampling: during exposure time an electricalcharge is accumulated

3 quantization of pixel values: the charge is converted to arange of integer values

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Digital image in microscopy: from light to pixelsImage quantizationImage histogram and contrast

Definition

Quantization is the process of mapping input values in acontinuous set to ouput values in a countable set (i.e. afinite number of elements).

In digital image processing the unit is bit per pixel (bpp).

12-bit sensor (i.e. 12 bpp) ⇒ values ∈ [0− 4095]

16-bit sensor (i.e. 16 bpp) ⇒ values ∈ [0− 65535]

16 bpp gives a better resolution than 12 bpp.

16-bit 3-bit

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Digital image in microscopy: from light to pixelsImage quantizationImage histogram and contrast

h(i) = the number of pixels with the intensity value i

8-bit image: i ∈ [0; 255]; 16-bit image: i ∈ [0; 65535]

Contrast: the difference between the image’s maximum andminimum pixel values

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Digital image in microscopy: from light to pixelsImage quantizationImage histogram and contrast

Contrast Contrast

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Digital image in microscopy: from light to pixelsImage quantizationImage histogram and contrast

In your acquisition software (e.g. micro-manager)

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

When it’s time to save your image...

Microscope with a 16-bit sensor, but datas are saved with 8bpp.

3 channels at 16 bpp resolution, but datas are saved asRGB (8-bit per channel).

loss of bit resolution ⇒ loss of information

check your software settings and your image meta-data(ImageJ, Icy...).

Rule

Image quantization must not be modified.

Exception

For large data sets acquisition (e.g. 3D +time), 8-bit resolutionleads to a size reduction of your files (by 2 comparing to 16-bit).

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

Background ”correction”

Images look better with a black background, but a lowintensity pixels truncation leads to a severe loss ofinformation.

Background is very usefull as reference for quantitativeanalysis or noise estimation.

We can perform better background correction.

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

Background correction

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

Background correction: comparison

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

Histogram saturation (cheating with the contrast)

The signal is too low, we can’t see anything of interest.

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

Contrast changes

For very low signal-to-noise ratio signals, you can enhancecontrast to obtain a better visualization in your acquisitionsoftware.

This is for visualization only.

Check your software settings and your image histogram(ImageJ, Icy...)

Rule

Always save your images without histogram saturation. The fullcontrast range must be retained.

Exception

No exception.

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

Pitfall related to quantization changesPitfall related to contrast changesFile format and compression

Choice of the file format

Use a file format with a meta-data storage capacity.

Image processing with ImageJ/Icy/CellProfiler : use theproprietary file format (lsm, czi,...).

Bio-formats from ”The Open Microscopy Environment”

The OME-TIFF format is also a good choice.

Choice of the compression algorithm

No compression for metrology.

Or lossless : PackBits, LZW.

Lossly compression like jpeg must not be avoided.

Arnold Fertin, Yves Usson Acquisition Pitfalls

IntroductionCommon pitfalls

Conclusion

About loss in image quantization (16 bpp ⇒ 8 bpp)

In practice, we can perform decent image processing with8-bit images.

Except signal processing with Fourier transform(cross-correlation, template matching, wavelet).

If you really need to save storage space, choose a fileformat with meta-data containing the original range.

About histogram saturation

You should not have any saturation after saving (ImageJ, Icy).

A good looking image is not equivalent to a goodmeasurement.

Histogram and meta-data are usefull, do not trust youreyes.

Arnold Fertin, Yves Usson Acquisition Pitfalls