Implementing the Diffeomorphic registration

bin/dipy_align_affine

@@ -0,0 +1,7 @@
+#!python
+
+from dipy.workflows.flow_runner import run_flow
+from dipy.workflows.align import ImageRegistrationFlow
+
+if __name__ == "__main__":
+    run_flow(ImageRegistrationFlow())

bin/dipy_align_syn

@@ -0,0 +1,7 @@
+#!python
+
+from dipy.workflows.flow_runner import run_flow
+from dipy.workflows.align import SynRegistrationFlow
+
+if __name__ == "__main__":
+    run_flow(SynRegistrationFlow())

bin/dipy_apply_affine

@@ -0,0 +1,7 @@
+#!python
+
+from dipy.workflows.flow_runner import run_flow
+from dipy.workflows.align import ApplyAffineFlow
+
+if __name__ == "__main__":
+    run_flow(ApplyAffineFlow())

bin/dipy_syn_register

@@ -0,0 +1,6 @@
+#!python
+from dipy.workflows.flow_runner import run_flow
+from dipy.workflows.align import SynRegistrationFlow
+
+if __name__ == "__main__":
+    run_flow(SynRegistrationFlow())

dipy/align/imaffine.py

@@ -954,7 +954,7 @@ def _init_optimizer(self, static, moving, transform, params0,
 
     def optimize(self, static, moving, transform, params0,
                  static_grid2world=None, moving_grid2world=None,
-                 starting_affine=None):
+                 starting_affine=None, ret_metric=False):
         r''' Starts the optimization process
 
         Parameters
@@ -993,11 +993,20 @@ def optimize(self, static, moving, transform, params0,
             If None:
                 Start from identity.
             The default is None.
+        ret_metric : boolean, optional
+            if True, it returns the parameters for measuring the
+            similarity between the images (default 'False').
+            The metric containing optimal parameters and
+            the distance between the images.
 
         Returns
         -------
         affine_map : instance of AffineMap
             the affine resulting affine transformation
+        xopt : optimal parameters
+            the optimal parameters (translation, rotation shear etc.)
+        fopt : Similarity metric
+            the value of the function at the optimal parameters.
         '''
         self._init_optimizer(static, moving, transform, params0,
                              static_grid2world, moving_grid2world,
@@ -1074,6 +1083,8 @@ def optimize(self, static, moving, transform, params0,
             self.params0 = self.transform.get_identity_parameters()
 
         affine_map.set_affine(self.starting_affine)
+        if ret_metric:
+            return affine_map, opt.xopt, opt.fopt
         return affine_map
 
 

dipy/io/image.py

@@ -1,14 +1,15 @@
 from __future__ import division, print_function, absolute_import
 
 import nibabel as nib
+import numpy as np
 
 
 def load_nifti(fname, return_img=False, return_voxsize=False,
                return_coords=False):
     img = nib.load(fname)
     data = img.get_data()
     vox_size = img.header.get_zooms()[:3]
-    
+
     ret_val = [data, img.affine]
 
     if return_img:
@@ -24,3 +25,44 @@ def load_nifti(fname, return_img=False, return_voxsize=False,
 def save_nifti(fname, data, affine, hdr=None):
     result_img = nib.Nifti1Image(data, affine, header=hdr)
     result_img.to_filename(fname)
+
+
+def load_affine_matrix(fname):
+    """
+        Parameters
+        ---------
+        fname : str
+            The file containing the saved affine matrix.
+    """
+    return np.loadtxt(fname)
+
+
+def save_affine_matrix(fname, affine):
+    """
+    Parameters
+    ---------
+    fname : string
+        File name to save the affine matrix.
+    affine : numpy array
+        The object containing the affine matrix.
+    """
+    np.savetxt(fname, affine)
+
+
+def save_quality_assur_metric(fname, xopt, fopt):
+    """
+    Parameters
+    __________
+    fname: string
+        File name to save the metric values.
+    xopt: numpy array
+        The metric containing the
+        optimal parameters for
+        image registration.
+    fopt: int
+        The distance between the registered images.
+    """
+    np.savetxt(fname, xopt, header="Optimal Parameter metric")
+    with open(fname, 'a') as f:
+        f.write('# Distance after registration\n')
+        f.write(str(fopt))