  Recently, Big Data applications have rapidly expanded into different
industries. Healthcare is also one the industries willing to use big data
platforms so that some big data analytics tools have been adopted in this field
to some extent. Medical imaging which is a pillar in diagnostic healthcare
deals with high volume of data collection and processing. A huge amount of 3D
and 4D images are acquired in different forms and resolutions using a variety
of medical imaging modalities. Preprocessing and analyzing imaging data is
currently a long process and cost and time consuming. However, not many big
data platforms have been provided or redesigned for medical imaging purposes
because of some restrictions such as data format. In this paper, we designed,
developed and successfully tested a new pipeline for medical imaging data
(especially functional magnetic resonance imaging - fMRI) using Big Data Spark
/ PySpark platform on a single node which allows us to read and load imaging
data, convert them to Resilient Distributed Datasets in order manipulate and
perform in-memory data processing in parallel and convert final results to
imaging format while the pipeline provides an option to store the results in
other formats such as data frame. Using this new solution and pipeline, we
repeated our previous works in which we extracted brain networks from fMRI data
using template matching and sum of squared differences (SSD) method. The final
results revealed our Spark (PySpark) based solution improved the performance
(in terms of processing time) around 4 times on a single compared to the
previous work developed in Python.
