  The growth of the amount of medical image data produced on a daily basis in
modern hospitals forces the adaptation of traditional medical image analysis
and indexing approaches towards scalable solutions. The number of images and
their dimensionality increased dramatically during the past 20 years. We
propose solutions for large-scale medical image analysis based on parallel
computing and algorithm optimization. The MapReduce framework is used to speed
up and make possible three large-scale medical image processing use-cases: (i)
parameter optimization for lung texture segmentation using support vector
machines, (ii) content-based medical image indexing, and (iii)
three-dimensional directional wavelet analysis for solid texture
classification. A cluster of heterogeneous computing nodes was set up in our
institution using Hadoop allowing for a maximum of 42 concurrent map tasks. The
majority of the machines used are desktop computers that are also used for
regular office work. The cluster showed to be minimally invasive and stable.
The runtimes of each of the three use-case have been significantly reduced when
compared to a sequential execution. Hadoop provides an easy-to-employ framework
for data analysis tasks that scales well for many tasks but requires
optimization for specific tasks.
