This repository is created to share the codes for ISBI 2018 paper with the title of "UNSUPERVISED DISCOVERY OF TOXOPLASMA GONDII MOTILITY PHENOTYPES"

We implemented our pipeline using Python 3 and associated scientific computing libraries (NumPy, SciPy, scikit-learn, matplotlib) .The core of our tracking algorithm used a combination of tools available in the OpenCV 3.1 computer vision library.

To use the code, First the coordinates of the cell trajectories should be extracted using a tracking algorithm .( Here we extracted them through our KLT based tracking algorithm and saved them in text file ). we have 6 files for 2 sets of Data:

• XA : Denotes the ** X ** coordinations on After Calcium data set.
• YA : Denotes the ** Y ** coordinations on After Calcium data set.
• XB : Denotes the ** X ** coordinations on Before Calcium data set.
• YB : Denotes the ** Y ** coordinations on Before Calcium data set.
• AA : Denotes the ** angles ** of the objects movement in 2 consecutive frames on After Calcium data set.
• AB : Denotes the ** angles ** of the objects movement in 2 consecutive frames on Before Calcium data set.

Then you should run the RBF_Clustering.py to load the files, Extract the AR parameters, making the RBF Kernel using eigenvectors and finally cluster the trajectories.

After running RBF_Clustering.py It will create a label.txt file in the same directory. This file shows the labels for each Trajectory and will be used in RBF_Visualization.py to visualize the clusters of the trajectories.