Merge pull request #318 from JoostJM/filter-out-nans

Add --skip-nans command line argument

radiomics/scripts/commandline.py

@@ -8,6 +8,7 @@
 import os.path
 import sys
 
+import numpy
 import six
 
 import radiomics
@@ -25,6 +26,8 @@
                          'Default is "txt": one feature per line in format "name:value". For "csv": one row of feature '
                          'names, followed by one row of feature values. For "json": Features are written in a JSON '
                          'format dictionary "{name:value}"')
+parser.add_argument('--skip-nans', action='store_true',
+                    help='Add this argument to skip returning features that have an invalid result (NaN)')
 parser.add_argument('--param', '-p', metavar='FILE', type=str, default=None,
                     help='Parameter file containing the settings to be used in extraction')
 parser.add_argument('--label', '-l', metavar='N', default=None, type=int,
@@ -43,6 +46,7 @@
 parser.add_argument('--version', action='version', help='Print version and exit',
                     version='%(prog)s ' + radiomics.__version__)
 
+
 def main():
   args = parser.parse_args()
 
@@ -79,6 +83,13 @@ def main():
 
     featureVector.update(extractor.execute(args.image, args.mask, args.label))
 
+    # if specified, skip NaN values
+    if args.skip_nans:
+      for key in list(featureVector.keys()):
+        if isinstance(featureVector[key], float) and numpy.isnan(featureVector[key]):
+          logger.debug('Feature %s computed NaN, removing from results', key)
+          del featureVector[key]
+
     if args.format == 'csv':
       writer = csv.writer(args.out, lineterminator='\n')
       writer.writerow(list(featureVector.keys()))

radiomics/scripts/commandlinebatch.py

@@ -7,6 +7,8 @@
 import logging
 import os.path
 
+import numpy
+
 import radiomics
 from radiomics import featureextractor
 
@@ -22,6 +24,8 @@
                     'Default is "csv": one row of feature names, followed by one row of feature values for each '
                     'image-mask combination. For "json": Features are written in a JSON format dictionary '
                     '"{name:value}", one line per image-mask combination')
+parser.add_argument('--skip-nans', action='store_true',
+                    help='Add this argument to skip returning features that have an invalid result (NaN)')
 parser.add_argument('--param', '-p', metavar='FILE', type=str, default=None,
                     help='Parameter file containing the settings to be used in extraction')
 parser.add_argument('--label', '-l', metavar='N', default=None, type=int,
@@ -131,6 +135,13 @@ def main():
 
         featureVector.update(extractor.execute(imageFilepath, maskFilepath, label))
 
+        # if specified, skip NaN values
+        if args.skip_nans:
+          for key in list(featureVector.keys()):
+            if isinstance(featureVector[key], float) and numpy.isnan(featureVector[key]):
+              logger.debug('Feature %s computed NaN, removing from results', key)
+              del featureVector[key]
+
         if args.format == 'csv':
           writer = csv.writer(args.outFile, lineterminator='\n')
           if headers is None:
@@ -139,7 +150,7 @@ def main():
 
           row = []
           for h in headers:
-            row.append(featureVector.get(h, "N/A"))
+            row.append(featureVector.get(h, ""))
           writer.writerow(row)
         elif args.format == 'json':
           json.dump(featureVector, args.out)