FIX: Improved mask estimation

Author: effigies

nipype/algorithms/confounds.py

@@ -1280,19 +1280,21 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
         mask = None
         for filename in mask_files:
             img = nb.load(filename)
+            img_as_mask = np.asanyarray(img.dataobj).astype("int32") > 0
             if mask is None:
-                mask = img.get_fdata() > 0
-            np.logical_or(mask, img.get_fdata() > 0, mask)
+                mask = img_as_mask
+            np.logical_or(mask, img_as_mask, mask)
         img = nb.Nifti1Image(mask, img.affine, header=img.header)
         return [img]
 
     if mask_method == "intersect":
         mask = None
         for filename in mask_files:
             img = nb.load(filename)
+            img_as_mask = np.asanyarray(img.dataobj).astype("int32") > 0
             if mask is None:
-                mask = img.get_fdata() > 0
-            np.logical_and(mask, img.get_fdata() > 0, mask)
+                mask = img_as_mask
+            np.logical_and(mask, img_as_mask, mask)
         img = nb.Nifti1Image(mask, img.affine, header=img.header)
         return [img]
 

nipype/algorithms/metrics.py

@@ -112,13 +112,13 @@ def _eucl_cog(self, nii1, nii2):
         from scipy.ndimage.measurements import center_of_mass, label
 
         origdata1 = np.asanyarray(nii1.dataobj)
-        origdata1 = (origdata1 != 0) & ~np.isnan(origdata1)
-        cog_t = np.array(center_of_mass(origdata1.copy())).reshape(-1, 1)
+        origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)
+        cog_t = np.array(center_of_mass(origdata1)).reshape(-1, 1)
         cog_t = np.vstack((cog_t, np.array([1])))
         cog_t_coor = np.dot(nii1.affine, cog_t)[:3, :]
 
         origdata2 = np.asanyarray(nii2.dataobj)
-        origdata2 = (origdata2 != 0) & ~np.isnan(origdata2)
+        origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)
         (labeled_data, n_labels) = label(origdata2)
 
         cogs = np.ones((4, n_labels))
@@ -165,13 +165,13 @@ def _eucl_max(self, nii1, nii2):
         from scipy.spatial.distance import cdist
 
         origdata1 = np.asanyarray(nii1.dataobj)
-        origdata1 = np.logical_not(np.logical_or(origdata1 == 0, np.isnan(origdata1)))
+        origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)
         origdata2 = np.asanyarray(nii2.dataobj)
-        origdata2 = np.logical_not(np.logical_or(origdata2 == 0, np.isnan(origdata2)))
+        origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)
 
         if isdefined(self.inputs.mask_volume):
             maskdata = np.asanyarray(nb.load(self.inputs.mask_volume).dataobj)
-            maskdata = np.logical_not(np.logical_or(maskdata == 0, np.isnan(maskdata)))
+            maskdata = (np.rint(maskdata) != 0) & ~np.isnan(maskdata)
             origdata1 = np.logical_and(maskdata, origdata1)
             origdata2 = np.logical_and(maskdata, origdata2)