def read_oct_volume(self):
""" Reads OCT data.
Returns:
obj:OCTVolumeWithMetaData
"""
if b'@IMG_SCAN_03' not in self.chunk_dict:
raise ValueError(
'Could not find OCT header @IMG_SCAN_03 in chunk list')
with open(self.filepath, 'rb') as f:
chunk_location, chunk_size = self.chunk_dict[b'@IMG_SCAN_03']
f.seek(chunk_location)
raw = f.read(22)
oct_header = self.oct_header.parse(raw)
number_pixels = oct_header.width * oct_header.height * oct_header.number_slices
raw_volume = np.fromstring(f.read(number_pixels * 2),
dtype=np.uint16)
volume = np.array(raw_volume)
volume = volume.reshape(oct_header.width,
oct_header.height,
oct_header.number_slices,
order='F')
volume = np.transpose(volume, [1, 0, 2])
oct_volume = OCTVolumeWithMetaData(
[volume[:, :, i] for i in range(volume.shape[2])])
return oct_volume
def read_oct_volume(self, interlaced=False):
""" Reads OCT data.
Args:
interlaced (bool): Determines whether data needs to be de-interlaced.
Returns:
obj:OCTVolumeWithMetaData
"""
with open(self.filepath, 'rb') as f:
volume = np.frombuffer(
f.read(), dtype=np.uint8
) # np.fromstring() gives numpy depreciation warning
num_slices = len(volume) // (1024 * 512)
volume = volume.reshape((1024, 512, num_slices), order='F')
if interlaced:
shape = volume.shape
interlaced = np.zeros(
(int(shape[0] / 2), shape[1], shape[2] * 2))
interlaced[..., 0::2] = volume[:512, ...]
interlaced[..., 1::2] = volume[512:, ...]
interlaced = np.rot90(interlaced, axes=(0, 1))
volume = interlaced
oct_volume = OCTVolumeWithMetaData(
[volume[:, :, i] for i in range(volume.shape[2])])
return oct_volume
def read_oct_volume(self):
""" Reads OCT data.
Returns:
obj:OCTVolumeWithMetaData
"""
if b'@IMG_JPEG' not in self.chunk_dict:
raise ValueError(
'Could not find OCT header @IMG_JPEG in chunk list')
with open(self.filepath, 'rb') as f:
chunk_location, chunk_size = self.chunk_dict[b'@IMG_JPEG']
f.seek(chunk_location) # Set the chunk’s current position.
raw = f.read(25)
oct_header = self.oct_header.parse(raw)
volume = np.zeros((oct_header.height, oct_header.width,
oct_header.number_slices))
for i in range(oct_header.number_slices):
size = np.frombuffer(f.read(4), dtype=np.int32)[0]
raw_slice = f.read(size)
slice = decode(raw_slice)
volume[:, :, i] = slice
oct_volume = OCTVolumeWithMetaData(
[volume[:, :, i] for i in range(volume.shape[2])])
return oct_volume
def read_oct_volume(self):
""" Reads OCT data.
Returns:
obj:OCTVolumeWithMetaData
"""
import pydicom
dicom_data = pydicom.dcmread(self.filepath)
pixel_data = dicom_data.pixel_array
oct_volume = OCTVolumeWithMetaData(volume=pixel_data)
return oct_volume
def load_oct_volume(self):
volFrames = np.reshape(self.frames.data, self.vol_frames_shape)
try:
with open(self.filepath, 'rb') as f:
for t, v in enumerate(volFrames):
for z, frame in enumerate(v):
self.vol[t, z, :, :] = frame.load(f, self.frames.geom)
self.loaded = True
except Exception as e:
print(e)
print('Stopping load')
return [
OCTVolumeWithMetaData(self.vol[t, :, :, :])
for t in range(self.vol.shape[0])
]
def read_oct_volume(self):
""" Reads OCT data.
Returns:
obj:OCTVolumeWithMetaData
"""
with open(self.filepath, 'rb') as f:
volume = np.fromstring(f.read(), dtype=np.uint8)
volume = volume.reshape((1024, 512, 128), order='F')
shape = volume.shape
# volume = np.transpose(volume, [2, 1, 0])
interlaced = np.zeros((int(shape[0] / 2), shape[1], shape[2] * 2))
interlaced[..., 0::2] = volume[:512, ...]
interlaced[..., 1::2] = volume[512:, ...]
interlaced = np.rot90(interlaced, axes=(0, 1))
oct_volume = OCTVolumeWithMetaData([interlaced[:, :, i] for i in range(interlaced.shape[2])])
return oct_volume
def read_oct_volume(self):
""" Reads OCT data.
Returns:
obj:OCTVolumeWithMetaData
"""
with open(self.filepath, 'rb') as f:
raw = f.read(36)
header = self.header_structure.parse(raw)
raw = f.read(52)
main_directory = self.main_directory_structure.parse(raw)
# traverse list of main directories in first pass
directory_stack = []
current = main_directory.current
while current != 0:
directory_stack.append(current)
f.seek(current)
raw = f.read(52)
directory_chunk = self.main_directory_structure.parse(raw)
current = directory_chunk.prev
# traverse in second pass and get all subdirectories
chunk_stack = []
volume_dict = {}
for position in directory_stack:
f.seek(position)
raw = f.read(52)
directory_chunk = self.main_directory_structure.parse(raw)
for ii in range(directory_chunk.num_entries):
raw = f.read(44)
chunk = self.sub_directory_structure.parse(raw)
volume_string = '{}_{}_{}'.format(chunk.patient_id,
chunk.study_id,
chunk.series_id)
if volume_string not in volume_dict.keys():
volume_dict[volume_string] = chunk.slice_id / 2
elif chunk.slice_id / 2 > volume_dict[volume_string]:
volume_dict[volume_string] = chunk.slice_id / 2
if chunk.start > chunk.pos:
chunk_stack.append([chunk.start, chunk.size])
# initalise dict to hold all the image volumes
volume_array_dict = {}
for volume, num_slices in volume_dict.items():
if num_slices > 0:
volume_array_dict[volume] = [0] * int(num_slices)
# traverse all chunks and extract slices
for start, pos in chunk_stack:
f.seek(start)
raw = f.read(60)
chunk = self.chunk_structure.parse(raw)
if chunk.type == 1073741824: # image data
raw = f.read(20)
image_data = self.image_structure.parse(raw)
if chunk.ind == 0: # fundus data
pass
# raw_volume = [struct.unpack('H', f.read(2))[0] for pixel in range(height*width)]
# image = np.array(raw_volume).reshape(height,width)
# plt.imshow(image)
elif chunk.ind == 1: # oct data
all_bits = [
f.read(2) for i in range(image_data.height *
image_data.width)
]
raw_volume = list(map(self.read_custom_float,
all_bits))
image = np.array(raw_volume).reshape(
image_data.width, image_data.height)
image = 256 * pow(image, 1.0 / 2.4)
volume_string = '{}_{}_{}'.format(
chunk.patient_id, chunk.study_id, chunk.series_id)
if volume_string in volume_array_dict.keys():
volume_array_dict[volume_string][
int(chunk.slice_id / 2) - 1] = image
else:
print('Failed to save image data for volume {}'.
format(volume_string))
oct_volumes = []
for key, volume in volume_array_dict.items():
oct_volumes.append(
OCTVolumeWithMetaData(volume=volume, patient_id=key))
return oct_volumes
def read_oct_volume(self):
""" Reads OCT data.
Returns:
obj:OCTVolumeWithMetaData
"""
with open(self.filepath, 'rb') as f:
raw = f.read(36)
header = self.header_structure.parse(raw)
raw = f.read(52)
main_directory = self.main_directory_structure.parse(raw)
# traverse list of main directories in first pass
directory_stack = []
current = main_directory.current
while current != 0:
directory_stack.append(current)
f.seek(current)
raw = f.read(52)
directory_chunk = self.main_directory_structure.parse(raw)
current = directory_chunk.prev
# traverse in second pass and get all subdirectories
chunk_stack = []
volume_dict = {}
for position in directory_stack:
f.seek(position)
raw = f.read(52)
directory_chunk = self.main_directory_structure.parse(raw)
for ii in range(directory_chunk.num_entries):
raw = f.read(44)
chunk = self.sub_directory_structure.parse(raw)
volume_string = '{}_{}_{}'.format(chunk.patient_id,
chunk.study_id,
chunk.series_id)
if volume_string not in volume_dict.keys():
volume_dict[volume_string] = chunk.slice_id / 2
elif chunk.slice_id / 2 > volume_dict[volume_string]:
volume_dict[volume_string] = chunk.slice_id / 2
if chunk.start > chunk.pos:
chunk_stack.append([chunk.start, chunk.size])
# initalise dict to hold all the image volumes
volume_array_dict = {}
volume_array_dict_additional = {
} # for storage of slices not caught by extraction
for volume, num_slices in volume_dict.items():
if num_slices > 0:
# num_slices + 1 here due to evidence that a slice was being missed off the end in extraction
volume_array_dict[volume] = [0] * int(num_slices + 1)
# traverse all chunks and extract slices
for start, pos in chunk_stack:
f.seek(start)
raw = f.read(60)
chunk = self.chunk_structure.parse(raw)
if chunk.type == 11: # laterality data
raw = f.read(20)
try:
laterality_data = self.lat_structure.parse(raw)
if laterality_data.laterality == 82:
self.laterality = 'R'
elif laterality_data.laterality == 76:
self.laterality = 'L'
except:
self.laterality = None
if chunk.type == 1073741824: # image data
raw = f.read(20)
image_data = self.image_structure.parse(raw)
if chunk.ind == 1: # oct data
all_bits = [
f.read(2) for i in range(image_data.height *
image_data.width)
]
raw_volume = list(map(self.read_custom_float,
all_bits))
image = np.array(raw_volume).reshape(
image_data.width, image_data.height)
image = 256 * pow(image, 1.0 / 2.4)
volume_string = '{}_{}_{}'.format(
chunk.patient_id, chunk.study_id, chunk.series_id)
if volume_string in volume_array_dict.keys():
volume_array_dict[volume_string][
int(chunk.slice_id / 2) - 1] = image
else:
# try to capture these additional images
if volume_string in volume_array_dict_additional.keys(
):
volume_array_dict_additional[
volume_string].append(image)
else:
volume_array_dict_additional[volume_string] = [
image
]
#print('Failed to save image data for volume {}'.format(volume_string))
oct_volumes = []
for key, volume in volume_array_dict.items():
oct_volumes.append(
OCTVolumeWithMetaData(volume=volume,
patient_id=key,
laterality=self.laterality))
for key, volume in volume_array_dict_additional.items():
oct_volumes.append(
OCTVolumeWithMetaData(volume=volume,
patient_id=key,
laterality=self.laterality))
return oct_volumes
