In this pyscro example we use opencv and matplotlib to automatically calculate an optimized parameter for thresholding the data based on various metrics.
In this pyscro example we use opencv and matplotlib to automatically calculate an optimized parameter
for thresholding the data based on various metrics. This technique ensures an
accurate and objective selection of the high intensity phase. Download the example, and load the hx file into Amira or Avizo. Then click the apply button of the optimizer module.
In some cases it is beneficial to optimize binarization
parameters by using the image itself to compute the threshold level (maximum entropy
or mutual information between greyscale and binary). The otsu method is a good example of this kind of auto-thresholding. The same methodology can be extrapolated to
other binarization algorithms (i.e. top-hat, watershed, ...) or binary mask manipulation (erosion,opening, ...)
associated with an image transformation (i.e. an image in which
the separation power will be calculated). The general principle of such a tool has been easily prototyped in a pyscro script.
The thresholding is done
based on OpenCV. Matplotlib allows to visualize the metric value based on the
current threshold used. The automated optimized parameter is at the maximum of the curve.