brainSimulator 0.5.4

Description:

brainSimulator 0.5.4

Functional brain image synthesis using the KDE or MVN distribution.
Currently in beta. Python code. Find the documentation at
http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure intended to
generate a new image set that share characteristics with an original
one. The system focuses on nuclear imaging modalities such as PET or
SPECT brain images. It analyses the dataset by applying PCA to the
original dataset, and then model the distribution of samples in the
projected eigenbrain space using a Probability Density Function (PDF)
estimator. Once the model has been built, anyone can generate new
coordinates on the eigenbrain space belonging to the same class, which
can be then projected back to the image space.

Use
With the new version, the whole interface has been switched to an
object. This allows to train the model once and then perform as many
sample drawings as required.
#navigate to the folder where simulator.py is located
import brainSimulator as sim

simulator = sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels)
images, classes = simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])


Cite
F.J. Martinez-Murcia et al (2017). “Functional Brain Imaging Synthesis
Based on Image Decomposition and Kernel Modelling: Application to
Neurodegenerative Diseases.” Frontiers in neuroinformatics (online).
DOI: 10.3389/fninf.2017.00065


Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental
to test the number of components (L) used in the modelling, otherwise it
will lead to overfitting. The KDE modelling works better `out of the
box’, but the results may be more disperse.


License
This code is released under the license
GPL-3.0+.