measure_model_msd

 MEASURE_MODEL_MSD: Measure the reference against a set of images using
                    mean-squared-differences and return mean.

 Code written by Katherine Smith, 2003

    GENERAL

      score = measure_model_msd(warped_images)

    INPUT/S

      -warped_images:
           A set of images (similar to the model).
           
    OUTPUT/S

      -score:
           Mean-squared difference score.

    PENDING WORK

      -

    KNOWN BUG/S

      -None.

    COMMENT/S

      -The name is misleading because models are not involved. Consider
      renaming.

    RELATED FUNCTION/S

      MSD, EVAL_MSD

    ABOUT

      -Created:     November 23rd, 2003
      -Last update: February 18th, 2004
      -Revision:    0.0.9
      -Author:      R. S. Schestowitz, University of Manchester
 ==============================================================

0001 function score = measure_model_msd(warped_images)
0002 % MEASURE_MODEL_MSD: Measure the reference against a set of images using
0003 %                    mean-squared-differences and return mean.
0004 %
0005 % Code written by Katherine Smith, 2003
0006 %
0007 %    GENERAL
0008 %
0009 %      score = measure_model_msd(warped_images)
0010 %
0011 %    INPUT/S
0012 %
0013 %      -warped_images:
0014 %           A set of images (similar to the model).
0015 %
0016 %    OUTPUT/S
0017 %
0018 %      -score:
0019 %           Mean-squared difference score.
0020 %
0021 %    PENDING WORK
0022 %
0023 %      -
0024 %
0025 %    KNOWN BUG/S
0026 %
0027 %      -None.
0028 %
0029 %    COMMENT/S
0030 %
0031 %      -The name is misleading because models are not involved. Consider
0032 %      renaming.
0033 %
0034 %    RELATED FUNCTION/S
0035 %
0036 %      MSD, EVAL_MSD
0037 %
0038 %    ABOUT
0039 %
0040 %      -Created:     November 23rd, 2003
0041 %      -Last update: February 18th, 2004
0042 %      -Revision:    0.0.9
0043 %      -Author:      R. S. Schestowitz, University of Manchester
0044 % ==============================================================
0045 
0046 n_images = size(warped_images,2); 
0047           % get number of images
0048 ref_image = warped_images(:,1);
0049           % set reference image
0050 for i=1:n_images
0051   score(i) = msd(ref_image, warped_images(:,i)); 
0052           % record the squared mean difference of all images against the reference
0053 end
0054 score = mean(score); 
0055           % get the mean score