| Περίληψη: | In this work were studied, implemented and evaluated two algorithms of image registration with two similarity metrics of mutual information. These were Viola-Wells Mutual Information [6],[7] and Mattes Mutual Information[11].
Materials and Methods: Two 3D MRI T1 and Τ2 brain images were used. The T1 image was rotated in all three axes , with the 27 possible triples of angles 0.25, 1.5 and 3 degrees and in the T2 image were added 3 Gaussian Noise Levels (1,3,5%). Thus were formed two experiments. The monomodal experiment which was registering the initial T1 image with its 27 rotated instances and the multimodal experiment which was registering the 4 T2 images (0,1,3,5% Gaussian Noise) with the 27 rotated T1 images. The registration framework had also a Regular Step Gradient Descent Optimizer, affine linear transformation and linear interpolator. After the 5 experimental set were registered with both algorithms, then in order for the results to be evaluated, 5 similarity metrics were used. These were: 1) Mean Square Difference 2) Correlation Coefficient 3) Joint Entropy 4) Normalized Mutual Information και 5) Entropy of the Difference Image. Finally t-test was applied, in order to find statistically significant differences.
Results: Both algorithms had similar outcome, although the algorithm with Mattes Μutual Information metric, had a slightly improved performance. Statistically important differences were found in the t-test.
Conclusions: The two methods should be tested more, using other kinds of transformation, and more data sets.
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