possible normalization for HD

updated in the inputs for normalization and display
also added the inner loop for normalization - but this one is likely
wrong

validation/crc_meanHausdorffDist.m

@@ -1,14 +1,17 @@
-function [mD,D12,D21] = crc_meanHausdorffDist(xyz1,xyz2)
+function [mD,D12,D21] = crc_meanHausdorffDist(varargin)
 % Calculate the mean Hausdorff distance between 2 surfaces, based on the
 % coordinates of the voxels of these surfaces.
 % This implementation only works with 3D volumes but is much faster than
 % standard implementations (with for-loops).
 %
 % FORMAT
-%   [mD,D12,D21] = crc_meanHausdorffDist(xyz1,xyz2)
+%   [mD,D12,D21] = crc_meanHausdorffDist(xyz1,xyz2,'normalize','true','display','false')
 %
 % INPUT
 %   xyz1/2  coordinates of voxels on borders in both images
+%   normalize is 'true' or 'false' (default)
+%             --> distances are normalized by the max
+%   display is 'true' of 'false' to show results with histogram
 % 
 % OUTPUT
 %   mD      mean distances between surface in images 1-2 and 2-1
@@ -18,10 +21,35 @@
 
 % Written by Christophe Phillips
 % University of Liege, Belgium
+% updated inputs and normalization C. Pernet , University of Edinburgh
 
-% Switch to true to get some stats and plots generated.
-fl_disp = false;
+%% Set defaults
 
+normalize = 0; % do not normalize
+display = false; % Switch to true to get some stats and plots generated.
+
+%% Check inputs
+xyz1 = varargin{1};
+xyz2 = varargin{2};
+
+not_recognized = 0;
+if nargin > 2
+    for ii=3:2:nargin
+       if strcmpi(varargin(ii),'normalize')
+           normalize = varargin{ii+1};
+       elseif strcmpi(varargin(ii),'normalize')
+           display = varargin{ii+1};
+       else
+           not_recognized = 1;
+       end           
+    end
+end
+
+if not_recognized == 1
+    disp('optional input not recognized, using defaults');
+end
+
+%% Compute
 % Make sure that the coordinates are in N x 3 format
 SZ1 = size(xyz1);
 if SZ1(1)<SZ1(2)
@@ -42,12 +70,16 @@
         (xyz2(:,3)-xyz1(ii,3)).^2 );             % voxels in 2nd image
     D12(ii) = min(Dtmp); % smallest distance from i_th voxel in 1st image
     D21 = min(D21,Dtmp); % smallest distance for all voxels in 2nd image
+    if normalize 
+       D12 = D12 ./ max(D12);
+       D21 = D21 ./ max(D21);
+    end
 end
 
 mD = [mean(D12) mean(D21)];
 
 % provide some stats and evaluation
-if fl_disp
+if display
     mM = [ min(D12) max(D12) median(D12) ; min(D21) max(D21) median(D12) ]; %#ok<*UNRCH>
     fprintf('\n')
     fprintf('D12 mean/min/max/median: %1.2f / %1.2f / %1.2f / %1.2f',mD(1),mM(1,:))
@@ -64,7 +96,7 @@
         [H21,bi] = hist(D21,Nbins);
         H12 = hist(D12,bi);
     end
-        figure, bar(bi,[H12' H21'])
+    figure, bar(bi,[H12' H21'])
     legend('D12','D21')
 end
 end

validation/image_overlap.m

@@ -198,7 +198,7 @@
 Bxyz1_mm = v2r(1:3,1:3)* [iBx1' ; iBy1' ; iBz1'];
 Bxyz2_mm = v2r(1:3,1:3)* [iBx2' ; iBy2' ; iBz2'];
 
-[mD,D12,D21] = crc_meanHausdorffDist(Bxyz1_mm,Bxyz2_mm); %#ok<*ASGLU>
+[mD,D12,D21] = crc_meanHausdorffDist(Bxyz1_mm,Bxyz2_mm,'normalize','true');
 mHd = mean(mD);
 
 %% Overlap measures to ground truth