Automated

Python/C++ port of the ImageJ extension TurboReg/StackReg written by Philippe Thevenaz/EPFL.

A python extension for the automatic alignment of a source image or a stack (movie) to a target image/reference frame.

This component can be used to find moving foreground features, which can be a powerful way to suppress false background detections in subsequent tracking steps.

set time window, and standard deviations above background for foreground time window should be more than 2x larger than time taken for a feature to traverse a pixel (NB. total window is 2x half-width +1) moving foreground identified by intensity increase relative to background average (i.e. median) for a pixel over a given time window "soft" segmentation, yielding foreground probability related to excess intensity (in standard deviations) over background level crude Anscombe transform applied to data to stabilize the variance

The freely available software module below is a 3D LoG filter. It applies a LoG (Laplacian of Gaussian or Mexican Hat) filter to a 2D image or to 3D volume. Here, we have a fast implementation. It is a perfect tool to enhance spots, like spherical particles, in noisy images. This module is easy to tune, only by selecting the standard deviations in X, Y and Z directions.

run("LoG 3D", "sigmax=1 sigmay=1 sigmaz=13 displaykernel=0 volume=1");

This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis.