THIS PROJECT IS NOW UNMAINTENED AND DEPRECATED
Objects detection and robust tracking for cell biology
scikit-tracker aims to be a robust Python library to work with cell biology microscopy images. OME XML and OME Tiff are supported to handle input/output to the lib. The two main goals of the library is to implement detection and tracking algorithms relevant to analyse biological microscopy dataset.
Several algorithms are featured and it is planned to add others:
Gaussian peak detection by deflation loop : Segré et al. Nature Methods (2008)
Cell boundary detection with bright field depth fitting : Julou, T., PNAS, (2013)
Cell nucleus segmentation : by Guillaume Gay
Lap Tracker, a robust single-particle tracking : K. Jaqaman and G. Danuser, Nature Methods, 2008. The version implemented in scikit-tracker is a slightly modified version from the original to allow easy, flexible and yet powerfull parameters adjustements with custom cost function.
scikit-tracker provides several intuitive graphical interfaces to semi-manually modify detected objects and trajectories (thanks to Qt4).
Current stable version is v0.2.
- Python >= 2.7 and >= 3.3
- numpy >= 1.8
- scipy >= 0.12
- pandas >= 0.13
- scikit-image >= 0.9
- scikit-learn >= 0.13
- matplotlib >= 1.3
- nose >= 1.3
- sphinx >= 1.2
- coverage >= 3.7
You can install scikit-tracker using pip:
$ pip install scikit-tracker
Or by cloning this repo and using setup.py:
$ git clone email@example.com:bnoi/scikit-tracker.git $ python setup.py install