Multimodal Medical Image Registration Using Particle Swarm Optimization with Influence of the Data’s Initial Orientation
Department of Computer Science
University of Erlangen-Nuremberg, Germany
Imaging techniques are an excellent example for the
continuous improvement of medical possibilities by technical innovation.
One particularly relevant field of research is multimodal
registration, where data sets must be aligned in order to make
their structures overlay. The overlay of the image positions is
reached by optimizing a similarity metric. A commonly used measure
applied in this process is the normalized mutual information,
which also will be used in this paper. Due to iteratively improving
this function value, a transformation can be determined, which
adapts the data in order to make the images overlap each other
as accurately as possible. For this improvement process, different
mathematical optimization methods are in use. One approach
is Particle Swarm Optimization (PSO), a nature-inspired optimization
technique. In the present work, four variants of the
PSO algorithm are presented and applied to medical image data.
Although classical PSO with standard parameters is shown to
have some limitations, considerable improvement can be obtained
by the modification of the calculation rules, the choice of the
parameter values and the choice of the objective function. Our
experimental results illustrate the substantial potential of PSO
for this type of application.
Proc. 12th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
(CIBCB); pp. 403-410, 2015.