changed first EIgenvariate to be positively correlated with mean

fsl_sbca.cc

@@ -62,7 +62,10 @@ using namespace std;
 		false, no_argument);
   Option<int> tc_order(string("--order"), 1,
 		string("        number of Eigenvariates (default 1)"),
-		false, requires_argument);		
+		false, requires_argument);	
+  Option<bool> abscc(string("--abscc"), false,
+		string("        use maximum absolute value instead of of maximum value of the cross-correlations"),
+		false, no_argument);			
   Option<bool> out_seeds(string("--out_seeds"), false,
 		string("output seed mask image as <basename>_seeds"),
 		false, no_argument);
@@ -291,6 +294,18 @@ ReturnMatrix calc_ttc(volume<float>& in){
 		DiagonalMatrix tmpD;
 	    EigenValues(Corr,tmpD,res);	
 		res = fliplr(res.Columns(res.Ncols()-tc_order.value()+1 , res.Ncols())) * std::sqrt(tmp.Nrows());	
+		
+		Matrix res2 = mean(tmp,2);
+
+		if(debug.value())
+			cerr << "DBG: mean size is " << res2.Nrows() << " x " << res2.Ncols() << endl;
+		res2 = res2.Column(1).t() * res.Column(1);
+		
+		if((float)res2.AsScalar() < 0){
+			res = -1.0 * res;
+			if(debug.value())
+				cerr << "DBG: flipping first eigenvariates" << endl;
+		}
 	}
 	
 	if(debug.value())
@@ -409,8 +424,14 @@ void calc_res(){
 	  out1.Row(ctr+1) = calc_tcorr(ctr);					
 
     for(int ctr = 1 ;ctr <= out1.Ncols(); ctr++){
-	  out1.Column(ctr).Maximum1(tmp2);
-      out2(1,ctr) = tmp2;
+	  if(!abscc.value()){
+	  	out1.Column(ctr).Maximum1(tmp2);
+      	out2(1,ctr) = tmp2;
+	  }else
+	  {
+	  	out1.Column(ctr).MaximumAbsoluteValue1(tmp2);
+      	out2(1,ctr) = tmp2;		
+	  }
     }
       
 	if(debug.value()){ 
@@ -515,6 +536,7 @@ int main(int argc,char *argv[]){
 		options.add(fnseeddata);
 		options.add(map_bin);
 		options.add(tc_mean);
+		options.add(abscc);
 		options.add(tc_order);
 		options.add(out_seeds);
 		options.add(out_seedmask);