Gradient Descent Optimization

To help with the optimization of the segmentation using the 

itk::WaveletTransform

I implemented a basic 

itk::WaveletGradientDescentOptimizer 

that takes into account each intensity profile separately. It gets from the 

itk::IntensityProfileMetric 

information about the gradient for each intensity profile around the shape or mesh being registered, then it creates using that information a new mesh based on those gradients and a step value. This new mesh is then approximated using wavelets by calling methods in the 

itk::WaveletTransform.

To test the new optimizer I tried tracing the brain with a tracing that has more detail than the ellipse model but less detail than the previous one I discussed. The following images shows information about the tracing that I used.

Then I ran the optimizer using the optimal parameters that I found from the tracing and the following movie shows the results. The movie illustrates how the fit on the right side of the brain is closer then with just the ellipse but on the left the coefficients seem to be standing still, which may be due to some bugs in the optimizer that I have to iron out.