ENH: Add support for using a fixed bin count

N.B. THIS IS NOT RECOMMENDED! See [this FAQ topic](http://pyradiomics.readthedocs.io/en/latest/faq.html#why-is-there-no-parameter-to-specify-a-fixed-bin-count) for more details.
AIM-Harvard · Jun 4, 2018 · c92e23b · c92e23b
1 parent e6435e7
commit c92e23b
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Showing 4 changed files with 37 additions and 8 deletions.
diff --git a/radiomics/base.py b/radiomics/base.py
@@ -66,10 +66,17 @@ def __init__(self, inputImage, inputMask, **kwargs):
     self.settings = kwargs
 
     self.binWidth = kwargs.get('binWidth', 25)
+    # USE OF BINCOUNT IS NOT RECOMMENDED! See documentation/FAQ/Input-Customization for more details.
+    self.binCount = kwargs.get('binCount', None)
     self.label = kwargs.get('label', 1)
     self.voxelBased = kwargs.get('voxelBased', False)
     self.initValue = kwargs.get('initValue', 0)
 
+    if self.binCount is not None:
+      self.logger.warning('Use of a fixed bin count is NOT RECOMMENDED! See '
+                          'http://pyradiomics.readthedocs.io/en/latest/faq.html#'
+                          'why-is-there-no-parameter-to-specify-a-fixed-bin-count for more details')
+
     self.coefficients = {}
 
     # all features are disabled by default
@@ -176,7 +183,8 @@ def _initCalculation(self):
   def _applyBinning(self):
     self.matrix, self.binEdges = imageoperations.binImage(self.binWidth,
                                                           self.imageArray,
-                                                          self.maskArray)
+                                                          self.maskArray,
+                                                          self.settings.get('binCount', None))
     self.coefficients['grayLevels'] = numpy.unique(self.matrix[self.maskArray])
     self.coefficients['Ng'] = int(numpy.max(self.coefficients['grayLevels']))  # max gray level in the ROI
 

diff --git a/radiomics/firstorder.py b/radiomics/firstorder.py
@@ -37,6 +37,7 @@ def __init__(self, inputImage, inputMask, **kwargs):
   def _initCalculation(self):
     super(RadiomicsFirstOrder, self)._initSegmentBasedCalculation()
     self.targetVoxelArray = self.imageArray[self.labelledVoxelCoordinates].astype('float')
+    self.discretizedTargetVoxelArray = None  # Lazy instantiation
 
     self.logger.debug('First order feature class initialized')
 
@@ -52,6 +53,18 @@ def _moment(self, a, moment=1, axis=0):
       s = numpy.power((a - mn), moment)
       return numpy.mean(s, axis)
 
+  def _getDiscretizedTargetVoxelArray(self):
+    if self.discretizedTargetVoxelArray is None:
+      if self.binCount is not None:
+        self.logger.debug('Using fixed bin count of %d bins. NOT RECOMMENDED!!', self.binCount)
+        binEdges = self.binCount
+      else:
+        binEdges = imageoperations.getBinEdges(self.binWidth, self.targetVoxelArray)
+
+      self.discretizedTargetVoxelArray = numpy.histogram(self.targetVoxelArray, binEdges)[0]
+
+    return self.discretizedTargetVoxelArray
+
   def getEnergyFeatureValue(self):
     r"""
     **1. Energy**
@@ -116,8 +129,7 @@ def getEntropyFeatureValue(self):
     """
 
     eps = numpy.spacing(1)
-    binEdges = imageoperations.getBinEdges(self.binWidth, self.targetVoxelArray)
-    bins = numpy.histogram(self.targetVoxelArray, binEdges)[0]
+    bins = self._getDiscretizedTargetVoxelArray()
 
     sumBins = bins.sum()
     if sumBins == 0:  # No segmented voxels
@@ -384,8 +396,7 @@ def getUniformityFeatureValue(self):
     """
 
     eps = numpy.spacing(1)
-    binEdges = imageoperations.getBinEdges(self.binWidth, self.targetVoxelArray)
-    bins = numpy.histogram(self.targetVoxelArray, binEdges)[0]
+    bins = self._getDiscretizedTargetVoxelArray()
     sumBins = bins.sum()
 
     if sumBins == 0:  # No segmented voxels

diff --git a/radiomics/imageoperations.py b/radiomics/imageoperations.py
@@ -52,7 +52,7 @@ def getBinEdges(binwidth, parameterValues):
   return binEdges  # numpy.histogram(parameterValues, bins=binedges)
 
 
-def binImage(binwidth, parameterMatrix, parameterMatrixCoordinates=None):
+def binImage(binwidth, parameterMatrix, parameterMatrixCoordinates=None, bincount=None):
   r"""
   Discretizes the parameterMatrix (matrix representation of the gray levels in the ROI) using the binEdges calculated
   using :py:func:`getBinEdges`. Only voxels defined by parameterMatrixCoordinates (defining the segmentation) are used
@@ -84,10 +84,16 @@ def binImage(binwidth, parameterMatrix, parameterMatrixCoordinates=None):
   logger.debug('Discretizing gray levels inside ROI')
 
   if parameterMatrixCoordinates is None:
-    binEdges = getBinEdges(binwidth, parameterMatrix[:])
+    if bincount is not None:
+      binEdges = numpy.histogram(parameterMatrix[:], bincount)[1]
+    else:
+      binEdges = getBinEdges(binwidth, parameterMatrix[:])
     parameterMatrix = numpy.digitize(parameterMatrix, binEdges)
   else:
-    binEdges = getBinEdges(binwidth, parameterMatrix[parameterMatrixCoordinates])
+    if bincount is not None:
+      binEdges = numpy.histogram(parameterMatrix[parameterMatrixCoordinates], bincount)
+    else:
+      binEdges = getBinEdges(binwidth, parameterMatrix[parameterMatrixCoordinates])
     parameterMatrix[parameterMatrixCoordinates] = numpy.digitize(parameterMatrix[parameterMatrixCoordinates], binEdges)
 
   return parameterMatrix, binEdges

diff --git a/radiomics/schemas/paramSchema.yaml b/radiomics/schemas/paramSchema.yaml
@@ -29,6 +29,10 @@ mapping:
         type: float
         range:
           min-ex: 0
+      binCount:
+        type: int
+        range:
+          min-ex: 0
       normalize:
         type: bool
       normalizeScale: