[Insight-users] Optimizer's Exception in Mattes Mutual Information Implementation

[Thomas - Kuiran Chen]

Hi Luis,

I am running into another interesting problem while using the Mattes' mutual information implementation with ultrasound images in ITK.  :-)

My question is: in which circumtances, will the optimizer throw an exception?  

In particular, for the Mattes implementation, the only change I made is I switched from one set of input images to another set of input images, while both sets are ultrasound images.  For one set of the inputs, the optimizer throwed the exception in the following position (with err = 0):

itkImageRegistrationMethod.txx:

try
    {
    // do the optimization
    m_Optimizer->StartOptimization();
    }
  catch( ExceptionObject& err )
    {
    // An error has occurred in the optimization.
    // Update the parameters
    m_LastTransformParameters = m_Optimizer->GetCurrentPosition();

    // Pass exception to caller
    throw err;
    }

The details symptoms are:

1. When I used 1st set of ultrasound images (2000 of them, each of 381x343 pixels), histogram bin size = 50, sampling rate = 1%, the Mattes mutual information implementation ran properly, without any programatic or runtime errors;

2. Then I used 2nd set of ultrasound images (2000 of them, each of 191x341 pixels), histogram bin size = 50, sampling rate = 1% or 10%
-  if I just use 200 images out of the 2000, the Mattes implementation worked with no problems (just as  in 1);
- if I choose to use more than 300 images out of the 2000, the above exception was thrown, with err = 0;  I even increased the sampling rate to 30%, but the problem persisted.

The two sets ultrasound images were captured using the same machine and same settings, but on different subjects: one with the soft tissues and one without.  The problem occured when I used the images of soft tissues.  But the only difference that I can tell between these two image sets are: 
- image size, and 
- different histogram distribution (images of soft tissue have more histogram variance).

What confused me is all of my 2000 ultrasound are basically similar in either intensity distribution or noise levels, but in some case (like few images through the loop) the program worked, but it didn't whe I increased the number of input images through the loop.

Kindly please enlighten me, :-).

Thanks very much!

Thomas Kuiran Chen