Continuing the thesis paper, “Evaluation of 14 nonlinear deformation algorithms applied
to human brain MRI registration” (Klein A, Evaluation of 14 nonlinear deformation algorithms
applied to human brain MRI registration. 2009`), corroborating previous registration data using an
expanded range of registration software, using our own lab provided Magnetic Resonance Images
(MRI), as well as comparing the resulting data using an expanded series range of image similarity
algorithms. This thesis paper will also deal with many tip and tricks related with dealing with
images before and after image registration as well comparing the processing time of each tool.
In this paper we will discuss all necessary ancillary information regarding MRI image
registration and normalization as well as a short introduction to the registration methods and image
similarity metrics used in this experiment. We will also discuss the methodology used in this
experiment including, using Brain Imaging Data Structure (BIDS) file format, different MRI image
formats, and scripts written in Python and MATLAB.
This thesis will show that although all registration methods have some merits, the
registration methods that best use the hardware available to them will tend to not only have better
results, but are more efficient and thus calculate quicker.

Continuing the thesis paper, “Evaluation of 14 nonlinear deformation algorithms applied
to human brain MRI registration” (Klein A, Evaluation of 14 nonlinear deformation algorithms
applied to human brain MRI registration. 2009`), corroborating previous registration data using an
expanded range of registration software, using our own lab provided Magnetic Resonance Images
(MRI), as well as comparing the resulting data using an expanded series range of image similarity
algorithms. This thesis paper will also deal with many tip and tricks related with dealing with
images before and after image registration as well comparing the processing time of each tool.
In this paper we will discuss all necessary ancillary information regarding MRI image
registration and normalization as well as a short introduction to the registration methods and image
similarity metrics used in this experiment. We will also discuss the methodology used in this
experiment including, using Brain Imaging Data Structure (BIDS) file format, different MRI image
formats, and scripts written in Python and MATLAB.
This thesis will show that although all registration methods have some merits, the
registration methods that best use the hardware available to them will tend to not only have better
results, but are more efficient and thus calculate quicker.

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