Add AUCCSH and fix features numeration

pyradiomics_radler/myfirstorder.py

@@ -62,7 +62,7 @@ class MyRadiomicsFirstOrder(MyRadiomicsFeaturesBase):
 
     def getTLGFeatureValue(self):
         r"""
-        **21. TLG**
+        **22. TLG**
 
         Total lesion glycolysis. 
         Defined as 
@@ -83,3 +83,46 @@ class MyRadiomicsFirstOrder(MyRadiomicsFeaturesBase):
 
         return numpy.mean(ROIPet[~numpy.isnan(ROIPet)]) * volume
 
+    def getAUCCSHFeatureValue(self):
+        r"""
+        **23. Inactive volume **
+
+        Area under the curve of the cumulative SUV-volume histogram describing the percentage 
+        of total tumour volume above a percentage threshold of maximum SUV, as defined by van Velden et al.
+
+        .. note::
+        Extracted from PET scans and not used in the CT feature set.
+        """
+        nBins = 1000  # By default
+        ROIPet = self.imageArray
+        mask = self.maskArray
+
+        ROIPet[~mask] = numpy.nan
+
+        outliers = numpy.where(
+            ROIPet
+            > (
+                numpy.mean(ROIPet[~numpy.isnan(ROIPet)])
+                + 3 * numpy.std(ROIPet[~numpy.isnan(ROIPet)])
+            )
+        )[0]
+
+        good_voxels = numpy.where(
+            ROIPet
+            <= (
+                numpy.mean(ROIPet[~numpy.isnan(ROIPet)])
+                + 3 * numpy.std(ROIPet[~numpy.isnan(ROIPet)])
+            )
+        )[0]
+
+        ROIPet[outliers] = numpy.mean(ROIPet[good_voxels])
+        ROIPet = ROIPet - numpy.min(ROIPet[~numpy.isnan(ROIPet)])
+        ROIPet = ROIPet / numpy.max(ROIPet[~numpy.isnan(ROIPet)])
+        volume = ROIPet[~numpy.isnan(ROIPet)]
+        nVoxel = len(volume)
+
+        bins, _ = numpy.histogram(volume, nBins)
+
+        csh = numpy.flipud(numpy.cumsum(numpy.flipud(bins)) / nVoxel)
+        return numpy.sum(csh / nBins)
+