ImageJ is an open source Java based image processing program extensively used in life sciences. The main functionality of the Turboreg plugin (http://bigwww.epfl.ch/thevenaz/turboreg/) consists of aligning or matching two images, one of them called the source image and the other the target image. The plugin is widely used in Neuroscience for pre-processing of both static and time-lapse imaging data.
After completion of the registration process, the plugin uses the final geometrical transformation of the source and target landmarks to create a warped image that has the size of the target and that contains a distorted version of the source. The rigid transformation is such that the source is well mapped to the target. TurboReg is well known to be robust to noisy data and fast, like the calcium images, thank to the multi-resolution approach and to smart optimization scheme. Nevertheless the limitations that the neuroscientists are faced with are the following:
- There is no export of the transformation which allows processing only of single channel images
- Limitation to process large datasets because the data should be in RAM
- No parallelized which limits the performance
- Limited possibility to tune the reference image
- No control the coherence of transformation over time, do not have any dynamic model of transformation.
- No constraints on the transformation. Typically, experimenters want to limit the transformation to a small drift.
The project will implement add-on functionality which will allow automation of pre-processing tasks and improved reproducibility of the results. The minimal goals of the project will be to:
- import and export registration transform settings
- add preview functionality
- add functionality to define the reference image based on some easy user-defined rules, e.g. the mean of the first 100 images
- iterate over the images, one image per files
- propagate transforms across different images/ fluorescent channels
- parallelization of the registration algorithm
- implement automatic testing mode for goodness of fit of registration.
The project is supported by the International Coordinating Neuroinformatics Facility
Mentors: Daniel Sage, EPFL
Dimiter Prodanov, INCF Belgian Node
Tomasz Konopczyński, INCF Belgian Node
Student Developer: Raghavender Sahdev
Working Period: June 2015 – present
Project Repository: https://github.com/incfbelgiannode/TimeLapseReg
The project was presented during the recent ImageJ User and Developers’ conference 2015 at the University of Wisconsin-Madison.
A talk was also given during the conference about the plugin. Check out links below-
to be updated soon!