remove depracated from compactness and add solidity feature

radiomics/shape.py

@@ -2,6 +2,7 @@ import numpy
 import SimpleITK as sitk
 
 from radiomics import base, cShape, deprecated
+from scipy.spatial import ConvexHull
 
 
 class RadiomicsShape(base.RadiomicsFeaturesBase):
@@ -203,7 +204,6 @@ class RadiomicsShape(base.RadiomicsFeaturesBase):
     """
         return self.Volume / (self.SurfaceArea ** (3.0 / 2.0) * numpy.sqrt(numpy.pi))
 
-    @deprecated
     def getCompactness2FeatureValue(self):
         r"""
     **6. Compactness 2**
@@ -414,6 +414,51 @@ class RadiomicsShape(base.RadiomicsFeaturesBase):
 
         return numpy.sqrt(1.0 - (minor_axis * least_axis / (major_axis ** 2)))
 
+    def getSolidityFeatureValue(self):
+        r"""
+    **18. Solidity**
+
+    This is a custom shape feature, implemented for comparison with Valieres et al.
+    Eccentricity is defined as: 
+
+    .. math::
+      \textit{Eccentricity} = \sqrt{1-\frac{a\times b}{c^2}}
+
+    Here, where c is the longest semi-principal axes of the ellipsoid,
+    and a and b are the second and third longest semi-principal axes of the ellipsoid.
+    """
+
+        ROIVolume = self.Volume
+
+        def tetrahedron_volume(a, b, c, d):
+            return (
+                numpy.abs(numpy.einsum('ij,ij->i', a - d, numpy.cross(b - d, c - d)))
+                / 6
+            )
+
+        Np = len(self.labelledVoxelCoordinates[0])
+        coordinates = numpy.array(self.labelledVoxelCoordinates, dtype='int').transpose(
+            (1, 0)
+        )  # Transpose equals zip(*a)
+        physicalCoordinates = coordinates * self.pixelSpacing[None, :]
+        physicalCoordinates -= numpy.mean(physicalCoordinates, axis=0)  # Centered at 0
+        physicalCoordinates /= numpy.sqrt(Np)
+        # import ipdb
+
+        # ipdb.set_trace()
+        ch = ConvexHull(physicalCoordinates)
+
+        simplices = numpy.column_stack(
+            (numpy.repeat(ch.vertices[0], ch.nsimplex), ch.simplices)
+        )
+        tets = ch.points[simplices]
+        a, b, c, d = tets[:, 0], tets[:, 1], tets[:, 2], tets[:, 3]
+        tetrahedron_vol = (
+            numpy.abs(numpy.einsum('ij,ij->i', a - d, numpy.cross(b - d, c - d))) / 6
+        )
+
+        return ROIVolume / numpy.sum(tetrahedron_vol)
+
     def _interpolate(self, grid, p1, p2):
         diff = (0.5 - self.maskArray[tuple(grid[p1])]) / (
             self.maskArray[tuple(grid[p2])] - self.maskArray[tuple(grid[p1])]