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Fix incorrect slicing during application of wavelet filter #182

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Jan 25, 2017
Merged

Fix incorrect slicing during application of wavelet filter #182

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21 changes: 11 additions & 10 deletions radiomics/imageoperations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,13 +251,13 @@ def applyThreshold(inputImage, lowerThreshold, upperThreshold, insideValue=None,
def swt3(inputImage, wavelet="coif1", level=1, start_level=0):
matrix = sitk.GetArrayFromImage(inputImage)
matrix = numpy.asarray(matrix)
data = matrix.copy()
if data.ndim != 3:
if matrix.ndim != 3:
raise ValueError("Expected 3D data array")

original_shape = matrix.shape
adjusted_shape = tuple([dim + 1 if dim % 2 != 0 else dim for dim in original_shape])
data = numpy.resize(data, adjusted_shape)
data = matrix.copy()
data.resize(adjusted_shape, refcheck=False)

if not isinstance(wavelet, pywt.Wavelet):
wavelet = pywt.Wavelet(wavelet)
Expand Down Expand Up @@ -309,7 +309,7 @@ def swt3(inputImage, wavelet="coif1", level=1, start_level=0):
def _decompose_i(data, wavelet):
# process in i:
H, L = [], []
i_arrays = chain.from_iterable(numpy.transpose(data, (0, 1, 2)))
i_arrays = chain.from_iterable(data)
for i_array in i_arrays:
cA, cD = pywt.swt(i_array, wavelet, level=1, start_level=0)[0]
H.append(cD)
Expand All @@ -321,27 +321,28 @@ def _decompose_i(data, wavelet):

def _decompose_j(data, wavelet):
# process in j:
s = data.shape
H, L = [], []
j_arrays = chain.from_iterable(numpy.transpose(data, (0, 1, 2)))
j_arrays = chain.from_iterable(numpy.transpose(data, (0, 2, 1)))
for j_array in j_arrays:
cA, cD = pywt.swt(j_array, wavelet, level=1, start_level=0)[0]
H.append(cD)
L.append(cA)
H = numpy.asarray([slice.T for slice in numpy.split(numpy.vstack(H), data.shape[0])])
L = numpy.asarray([slice.T for slice in numpy.split(numpy.vstack(L), data.shape[0])])
H = numpy.hstack(H).reshape((s[0], s[2], s[1])).transpose((0, 2, 1))
L = numpy.hstack(L).reshape((s[0], s[2], s[1])).transpose((0, 2, 1))
return H, L


def _decompose_k(data, wavelet):
# process in k:
H, L = [], []
k_arrays = chain.from_iterable(numpy.transpose(data, (1, 2, 0)))
k_arrays = chain.from_iterable(numpy.transpose(data, (2, 1, 0)))
for k_array in k_arrays:
cA, cD = pywt.swt(k_array, wavelet, level=1, start_level=0)[0]
H.append(cD)
L.append(cA)
H = numpy.dstack(H).reshape(data.shape)
L = numpy.dstack(L).reshape(data.shape)
H = numpy.asarray([slice for slice in numpy.split(numpy.vstack(H), data.shape[2])]).T
L = numpy.asarray([slice for slice in numpy.split(numpy.vstack(L), data.shape[2])]).T
return H, L


Expand Down