Avoiding Pitfalls During Image Acquisition...Avoiding Pitfalls During Image Acquisition Arnold...
Transcript of Avoiding Pitfalls During Image Acquisition...Avoiding Pitfalls During Image Acquisition Arnold...
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