Component

SIMPLETRACKER a simple particle tracking algorithm that can deal with gaps.

Tracking , or particle linking, consist in re-building the trajectories of one or several particles as they move along time. Their position is reported at each frame, but their identity is yet unknown: we do not know what particle in one frame corresponding to a particle in the previous frame. Tracking algorithms aim at providing a solution for this problem. 

simpletracker.m is - as the name says - a simple implementation of a tracking algorithm, that can deal with gaps. A gap happens when one particle that was detected in one frame is not detected in the subsequent one. If not dealt with, this generates a track break, or a gap, in the frame where the particle disappear, and a false new track in the frame where it re-appear. 

Mean square displacement (MSD) analysis is a technique commonly used in colloidal studies and biophysics to determine what is the mode of displacement of particles followed over time. In particular, it can help determine whether the particle is:

• freely diffusing;
• transported;
• bound and limited in its movement.

On top of this, it can also derive an estimate of the parameters of the movement, such as the diffusion coefficient.

@msdanalyzer is a MATLAB per-value class that helps performing this kind of analysis. The user provides several trajectories he measured, and the class can derive meaningful quantities for the determination of the movement modality, assuming that all particles follow the same movement model and sample the same environment.

Non linear registration intensity based for MRI brain exams. To be applied after FLIRT

FLIRT (FMRIB's Linear Image Registration Tool) is a fully automated robust and accurate tool for linear (affine) intra- and inter-modal brain image registration.

FLIRT comes with a main GUI as well as three supporting guis:

• ApplyXFM - for applying saved transformations and changing FOVs
• InvertXFM - for inverting saved transformations
• ConcatXFM - for concatenating saved transformations

A deep-learning solution for stain color normalization in digital histology images