Merge pull request #64 from ofgulban/devel

v1.4.0

.appveyor.yml

@@ -2,7 +2,7 @@ build: false
 
 environment:
   matrix:
-    - PYTHON_VERSION: 2.7
+    - PYTHON_VERSION: 3.6
       MINICONDA: C:\Miniconda
 
 init:
@@ -12,9 +12,9 @@ install:
   - "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
-  - "conda env create --force -f environment.yml python=2.7"
+  - "conda env create --force -f environment.yml python=3.6"
   - activate segmentator
-  - python setup.py develop
+  - python setup.py install
 
 test_script:
   - py.test

README.md

@@ -2,13 +2,19 @@
 
 # Segmentator
 
-<img src="logo/logo.png" width=420 align="right" />
+<img src="visuals/logo.png" width=420 align="right" />
 
 Segmentator is a free and open-source package for multi-dimensional data exploration and segmentation for 3D images. This application is mainly developed and tested using ultra-high field magnetic resonance imaging (MRI) brain data.
 
 
 The goal is to provide a complementary tool to the already available brain tissue segmentation methods (to the best of our knowledge) in other software packages (FSL, Freesurfer, SPM, Brainvoyager, itk-SNAP, etc.).
 
+### Citation:
+- Our preprint can be accessed from __[this link.](http://biorxiv.org/cgi/content/short/245738v1)__
+- Released versions of this package can be cited by using our __[Zenodo DOI](https://zenodo.org/badge/latestdoi/59303623).__
+
+<img src="visuals/animation_01.gif" width=840 align="center" />
+
 ## Core dependencies
 [**Python 2.7**](https://www.python.org/download/releases/2.7/)
 
@@ -43,7 +49,7 @@ segmentator /path/to/file.nii.gz
 segmentator --help
 ```
 
-Visit [our wiki](https://github.com/ofgulban/segmentator/wiki/Installation) to see alternative installation methods.
+Check out __[our wiki](https://github.com/ofgulban/segmentator/wiki)__ for further details such as [GUI controls](https://github.com/ofgulban/segmentator/wiki/Controls), [alternative installation methods](https://github.com/ofgulban/segmentator/wiki/Installation) and more...
 
 ## Support
 Please use [GitHub issues](https://github.com/ofgulban/segmentator/issues) for questions, bug reports or feature requests.

segmentator/__main__.py

@@ -7,21 +7,19 @@
 
 TODO: Argument parsing can be better structured, maybe by using parents. help
 looks a bit messy as is.
-
 """
 
-import sys
 import argparse
 import config as cfg
 from segmentator import __version__
 
 
-def main(args=None):
+def main():
     """Command line call argument parsing."""
-    if args is None:
-        args = sys.argv[1:]
-    # Main arguments
+    # Instantiate argument parser object:
     parser = argparse.ArgumentParser()
+
+    # Add arguments to namespace:
     parser.add_argument(
         'filename', metavar='path',
         help="Path to input. Mostly a nifti file with image data."
@@ -39,7 +37,7 @@ def main(args=None):
     parser.add_argument(
         "--scale", metavar=str(cfg.scale), required=False, type=float,
         default=cfg.scale,
-        help="Data is scaled from 0 to this number."
+        help="Determines nr of bins. Data is scaled between 0 to this number."
         )
     parser.add_argument(
         "--percmin", metavar=str(cfg.perc_min), required=False, type=float,
@@ -70,6 +68,10 @@ def main(args=None):
         "--include_zeros", action='store_true',
         help="Include image zeros in histograms. Not used by default."
         )
+    parser.add_argument(
+        "--export_gramag", action='store_true',
+        help="Export the gradient magnitude image. Not used by default."
+        )
 
     # used in ncut preparation  (TODO: not yet tested after restructuring.)
     parser.add_argument(
@@ -115,6 +117,7 @@ def main(args=None):
     cfg.cbar_init = args.cbar_init
     if args.include_zeros:
         cfg.discard_zeros = False
+    cfg.export_gramag = args.export_gramag
     # used in ncut preparation
     cfg.ncut_figs = args.ncut_figs
     cfg.max_rec = args.ncut_maxRec
@@ -129,13 +132,13 @@ def main(args=None):
 
     # Call other scripts with import method (couldn't find a better way).
     if args.nogui:
-        print '--No GUI option is selected. Saving 2D histogram image...'
+        print('--No GUI option is selected. Saving 2D histogram image...')
         import hist2d_counts
     elif args.ncut_prepare:
-        print '--Preparing N-cut related files...'
+        print('--Preparing N-cut related files...')
         import ncut_prepare
     elif args.ncut:
-        print '--Experimental N-cut feature is selected.'
+        print('--Experimental N-cut feature is selected.')
         import segmentator_ncut
     else:
         import segmentator_main

segmentator/config.py

@@ -18,6 +18,7 @@
 cbar_max = 5.0
 cbar_init = 3.0
 discard_zeros = True
+export_gramag = False
 
 # possible gradient magnitude computation keyword options
 gramag_options = ['scharr', 'sobel', 'prewitt', 'numpy']

segmentator/future/deriche.py

@@ -54,9 +54,9 @@
 gra_z = np.transpose(gra_z, (2, 0, 1))
 
 end = time.time()
-print 'Gradients are computed in:', (end - start), 'seconds'
+print('Gradients are computed in:', (end - start), 'seconds')
 
-print 'Saving the gradient magnitude image.'
+print('Saving the gradient magnitude image.')
 # put the data in 4D format and save
 temp = np.array([gra_x, gra_y, gra_z])
 temp = np.transpose(temp, (1, 2, 3, 0))
@@ -71,4 +71,4 @@
 outName = basename+'_deriche_a' + str(alpha) + '_graMag'
 outName = outName.replace('.', 'pt')
 save(out, outName + '.nii.gz')
-print 'Saved as:', outName
+print('Saved as:', outName)

segmentator/gui_utils.py

@@ -66,8 +66,22 @@ def remapMsks(self, remap_slice=True):
             self.volHistMaskH.set_extent((0, self.nrBins, self.nrBins, 0))
         # histogram to image mapping
         if remap_slice:
-            self.imaSlcMsk = map_2D_hist_to_ima(
-                self.invHistVolume[:, :, self.sliceNr], self.volHistMask)
+            temp_slice = self.invHistVolume[:, :, self.sliceNr]
+            image_slice_shape = self.invHistVolume[:, :, self.sliceNr].shape
+            if cfg.discard_zeros:
+                zmask = temp_slice != 0
+                image_slice_mask = map_2D_hist_to_ima(temp_slice[zmask],
+                                                      self.volHistMask)
+                # reshape to image slice shape
+                self.imaSlcMsk = np.zeros(image_slice_shape)
+                self.imaSlcMsk[zmask] = image_slice_mask
+            else:
+                image_slice_mask = map_2D_hist_to_ima(temp_slice.flatten(),
+                                                      self.volHistMask)
+                # reshape to image slice shape
+                self.imaSlcMsk = image_slice_mask.reshape(image_slice_shape)
+
+            # for optional border visualization
             if self.borderSwitch == 1:
                 self.imaSlcMsk = self.calcImaMaskBrd()
 
@@ -149,7 +163,7 @@ def findVoxInHist(self, event):
         pixel_x = int(np.floor(event.xdata))
         pixel_y = int(np.floor(event.ydata))
         aoi = self.invHistVolume[:, :, self.sliceNr]  # array of interest
-        # Check rotation (TODO: code repetition!)
+        # Check rotation
         cyc_rot = self.cycRotHistory[self.cycleCount][1]
         if cyc_rot == 1:  # 90
             aoi = np.rot90(aoi, axes=(0, 1))
@@ -181,7 +195,7 @@ def on_press(self, event):
                     if self.ctrlHeld is False:  # ctrl no
                         contains = self.contains(event)
                         if not contains:
-                            print 'cursor outside circle mask'
+                            print('cursor outside circle mask')
                         if not contains:
                             return
                         # get sector centre x and y positions
@@ -235,12 +249,12 @@ def on_press(self, event):
                     # the first click the entire field constitutes the subfield
                     counter = int(self.counterField[ybin][xbin])
                     if counter+1 >= self.ima_ncut_labels.shape[2]:
-                        print "already at maximum ncut dimension"
+                        print("already at maximum ncut dimension")
                         return
                     self.counterField[(
                         self.ima_ncut_labels[:, :, counter] ==
                         self.ima_ncut_labels[[ybin], [xbin], counter])] += 1
-                    print "counter:" + str(counter+1)
+                    print("counter:" + str(counter+1))
                     # define arrays with old and new labels for later indexing
                     oLabels = self.ima_ncut_labels[:, :, counter]
                     nLabels = self.ima_ncut_labels[:, :, counter+1]
@@ -373,7 +387,7 @@ def checkRotation(self):
 
     def exportNifti(self, event):
         """Export labels in the image browser as a nifti file."""
-        print "start exporting labels..."
+        print("Start exporting labels...")
         # put the permuted indices back to their original format
         cycBackPerm = (self.cycleCount, (self.cycleCount+1) % 3,
                        (self.cycleCount+2) % 3)
@@ -384,11 +398,19 @@ def exportNifti(self, event):
         for label, newLabel in zip(labels, intLabels):
             out_volHistMask[out_volHistMask == label] = intLabels[newLabel]
         # get 3D brain mask
-        temp = np.transpose(self.invHistVolume, cycBackPerm)
-        outNii = map_2D_hist_to_ima(temp, out_volHistMask)
-        outNii = outNii.reshape(temp.shape)
+        volume_image = np.transpose(self.invHistVolume, cycBackPerm)
+        if cfg.discard_zeros:
+            zmask = volume_image != 0
+            temp_labeled_image = map_2D_hist_to_ima(volume_image[zmask],
+                                                    out_volHistMask)
+            out_nii = np.zeros(volume_image.shape)
+            out_nii[zmask] = temp_labeled_image  # put back flat labels
+        else:
+            out_nii = map_2D_hist_to_ima(volume_image.flatten(),
+                                         out_volHistMask)
+            out_nii = out_nii.reshape(volume_image.shape)
         # save mask image as nii
-        new_image = Nifti1Image(outNii, header=self.nii.get_header(),
+        new_image = Nifti1Image(out_nii, header=self.nii.get_header(),
                                 affine=self.nii.get_affine())
         # get new flex file name and check for overwriting
         self.nrExports = 0
@@ -397,8 +419,8 @@ def exportNifti(self, event):
             self.nrExports += 1
             self.flexfilename = '_labels_' + str(self.nrExports) + '.nii.gz'
         save(new_image, self.basename + self.flexfilename)
-        print "successfully exported image labels as: \n" + \
-            self.basename + self.flexfilename
+        print("successfully exported image labels as: \n"
+              + self.basename + self.flexfilename)
 
     def clearOverlays(self):
         """Clear overlaid items such as circle highlights."""
@@ -478,14 +500,14 @@ def exportNyp(self, event):
             outFileName = outFileName.replace('identifier', 'volHistLabels')
             outFileName = outFileName.replace('.', 'pt')
             np.save(outFileName, self.volHistMask)
-            print "successfully exported histogram colors as: \n" + \
-                outFileName
+            print("Successfully exported histogram colors as: \n"
+                  + outFileName)
         elif self.segmType == 'main':
             outFileName = outFileName.replace('identifier', 'volHist')
             outFileName = outFileName.replace('.', 'pt')
             np.save(outFileName, self.counts)
-            print "successfully exported histogram counts as: \n" + \
-                outFileName
+            print("successfully exported histogram counts as: \n"
+                  + outFileName)
         else:
             return
 
@@ -655,14 +677,9 @@ def contains(self, event):
 
     def draw(self, ax, cmap='Reds', alpha=0.2, vmin=0.1,
              interpolation='nearest', origin='lower', extent=[0, 100, 0, 100]):
-        """Draw stuff."""
+        """Draw sector mask."""
         BinMask = self.binaryMask()
-        FigObj = ax.imshow(
-            BinMask,
-            cmap=cmap,
-            alpha=alpha,
-            vmin=vmin,
-            interpolation=interpolation,
-            origin=origin,
-            extent=extent)
+        FigObj = ax.imshow(BinMask, cmap=cmap, alpha=alpha, vmin=vmin,
+                           interpolation=interpolation, origin=origin,
+                           extent=extent)
         return (FigObj, BinMask)

segmentator/hist2d_counts.py

@@ -29,7 +29,7 @@
 
 # data processing
 orig = np.squeeze(nii.get_data())
-orig = truncate_range(orig, percMin=cfg.perc_min, percMax=cfg.perc_max)
+orig, _, _ = truncate_range(orig, percMin=cfg.perc_min, percMax=cfg.perc_max)
 orig = scale_range(orig, scale_factor=cfg.scale, delta=0.0001)
 gra = set_gradient_magnitude(orig, cfg.gramag)
 
@@ -44,4 +44,4 @@
            )
 outName = outName.replace('.', 'pt')
 np.save(outName, counts)
-print '----Image saved as:\n ' + outName
+print('----Image saved as:\n ' + outName)