pylsci 1.0.2

Python Package for Laser Speckle Contrast Imaging

PyLSCI

A Python Package for Laser Speckle Contrast Imaging.

It converts raw laser speckle data (as 2D or 3D NumPy arrays) to laser speckle contrast images (a 2D NumPy array).

PyLSCI on PyPI

---

The code for this package was developed for my thesis on Fingerprint Presentation Attack Detection using Laser Speckle Contrast Imaging:

• Keilbach, P., Kolberg, J., Gomez-Barrero, M., Busch, C., & Langweg, H. (2018). Fingerprint Presentation Attack Detection using Laser Speckle Contrast Imaging. International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, 2018, pp. 1-6, doi: https://10.23919/BIOSIG.2018.8552931.

Installation

pip install pylsci

Dependencies

The PyLSCI packages depends on NumPy, which is is used to do all the array related calculations.

Workflow

To work with this package, you need to have you laser speckle images available as 2D or 3D NumPy arrays:

• For spatial contrast calculation, PyLSCI expects a single, raw laser speckle image as a 2D NumPy Array.
• For temporal or spatio-temporal contrast calculation, PyLSCI expects a time series of multiple raw laser speckle images as a 3D NumPy Array.

Raw Laser Speckle Image (2D or 3D NumPy Array) :point_right: PyLSCI :point_right: Laser Speckle Contrast Image (2D NumPy Array)

The process of converting the raw laser speckle images to NumPy arrays is out of scope of the pylsci package, since this process is highly dependent on a particular LSCI setup. See the demo notebook for an example.

Usage

The following code provides a quick demo of how to use the PyLSCI package. For a more detailed documentation, please see the Jupyter Notebook demo.ipynb that is provided in this GitHub project.

from pylsci import Lsci
# for this example, it is assumed you have a custom helper module (here it is the util.py module)
# that is responsible for converting your raw laser speckle images to the NumPy arrays expected by PyLSCI
from util import convert_speckle_to_numpy

# 1. Loading Raw Laser Speckle Data to NumPy Arrays
# 
# speckle_img represents a single laser speckle image as 2D NumPy array for spatial contrast ccalculation
speckle_img = convert_speckle_to_numpy('img/spatial.tif')
# speckle_img_sequence represents a time series of speckle images as a NumPy 3D array 
# for temporal or spatio-temporal contrast calculation,
# where the first dimension is the number of images
speckle_img_sequence = convert_speckle_to_numpy('img/temporal.png', temporal_series=True)

# 2. Create a Lsci Object
# 
# you can pass custom values for the spatial and temporal neighborhoos arguments nbh_s and nbh_t.
# Their values default to nbh_s=3 and nbh_t=25 if you omit them.
# Note that nbh_s needs to be an odd value, but there is no constraint for nbh_t
lsci = Lsci(nbh_s=5, nbh_t=40)

# 3. Calculate the Laser Speckle Contrast Images
#
# 3.1 Spatial Contrast Calculation 
# The spatial contrast calculation requires a single laser speckle image as a NumPy 2D array
# and returns a single laser speckle contrast image as a 2D NumPy array.
s_lsci = lsci.spatial_contrast(speckle_img)

# 3.2 Temporal Contrast Calculation
# The temporal and spatio-temporal contrast calculation require a 3D NumPy array (time series of laser speckle images)
# and will return a single (averaged) laser speckle contrast image as a 2D NumPy array.
t_lsci = lsci.temporal_contrast(speckle_img_sequence)
st_lsci = lsci.spatio_temporal_contrast(speckle_img_sequence)

Implementation Details

Note that the iterations of the 2D or 3D arrays are not (yet) optimized. The temporal_contrast() function performs quite well, since NumPy allows to calculate the standard deviation and mean along the temporal axis for the whole array. This is not the case for the spatial_contrast() and spatio_temporal_contrast() functions, where the implementations rely on inefficient, nested loops.

Please be aware of this as calculating the contrast with the spatial_contrast() and spatio_temporal_contrast() functions may take a long time.

Further Reading

To understand the theory and concepts of LSCI, the following papers are recommended:

• Boas, D. A., & Dunn, A. K. (2010). Laser speckle contrast imaging in biomedical optics. Journal of biomedical optics, 15(1), 011109. https://doi.org/10.1117/1.3285504
• Vaz, P. G., Humeau-Heurtier, A., Figueiras, E., Correia, C., & Cardoso, J. (2016). Laser Speckle Imaging to Monitor Microvascular Blood Flow: A Review. IEEE reviews in biomedical engineering, 9, 106–120. https://doi.org/10.1109/RBME.2016.2532598
• Senarathna, J., Rege, A., Li, N., & Thakor, N. V. (2013). Laser Speckle Contrast Imaging: theory, instrumentation and applications. IEEE reviews in biomedical engineering, 6, 99–110. https://doi.org/10.1109/RBME.2013.2243140
• Briers, D., Duncan, D. D., Hirst, E., Kirkpatrick, S. J., Larsson, M., Steenbergen, W., Stromberg, T., & Thompson, O. B. (2013). Laser speckle contrast imaging: theoretical and practical limitations. Journal of biomedical optics, 18(6), 066018. https://doi.org/10.1117/1.JBO.18.6.066018