def extract_dicom_images_patient(src_dir):
target_dir = settings.NDSB3_EXTRACTED_IMAGE_DIR
print("Dir: ", src_dir)
dir_path = settings.NDSB3_RAW_SRC_DIR + src_dir + "/"
patient_id = src_dir
slices = load_patient(dir_path)
print(len(slices), "\t", slices[0].SliceThickness, "\t", slices[0].PixelSpacing)
print("Orientation: ", slices[0].ImageOrientationPatient)
assert slices[0].ImageOrientationPatient == [1.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000]
pixels = get_pixels_hu(slices)
image = pixels
print(image.shape)
invert_order = slices[1].ImagePositionPatient[2] > slices[0].ImagePositionPatient[2]
print("Invert order: ", invert_order, " - ", slices[1].ImagePositionPatient[2], ",", slices[0].ImagePositionPatient[2])
pixel_spacing = slices[0].PixelSpacing
pixel_spacing.append(slices[0].SliceThickness)
image = helpers.rescale_patient_images(image, pixel_spacing, settings.TARGET_VOXEL_MM)
if not invert_order:
image = numpy.flipud(image)
for i in range(image.shape[0]):
patient_dir = target_dir + patient_id + "/"
if not os.path.exists(patient_dir):
os.mkdir(patient_dir)
img_path = patient_dir + "img_" + str(i).rjust(4, '0') + "_i.png"
org_img = image[i]
img, mask = helpers.get_segmented_lungs(org_img.copy())
org_img = helpers.normalize_hu(org_img)
cv2.imwrite(img_path, org_img * 255)
cv2.imwrite(img_path.replace("_i.png", "_m.png"), mask * 255)
def extract_dicom_images_patient(src_dir):
target_dir = settings.NDSB3_EXTRACTED_IMAGE_DIR
#print("Dir: ", src_dir)
settings.log.info("Patient: {0}".format(src_dir))
dir_path = settings.NDSB3_RAW_SRC_DIR + src_dir + "/"
patient_id = src_dir
slices = load_patient(dir_path)
settings.log.info(
"Slice number: {0} \t thickness - {1} \t x and y spacing - {2}".format(
len(slices), slices[0].SliceThickness, slices[0].PixelSpacing))
#print(len(slices), "\t", slices[0].SliceThickness, "\t", slices[0].PixelSpacing)
settings.log.info("Orientation: {0}".format(
slices[0].ImageOrientationPatient))
#print("Orientation: ", slices[0].ImageOrientationPatient)
#assert slices[0].ImageOrientationPatient == [1.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000]
cos_value = (slices[0].ImageOrientationPatient[0])
cos_degree = round(math.degrees(math.acos(cos_value)), 2)
pixels = get_pixels_hu(slices)
image = pixels
settings.log.info("image shape: {0}".format(image.shape))
invert_order = slices[1].ImagePositionPatient[2] > slices[
0].ImagePositionPatient[2]
settings.log.info("Invert order: {0} - {1:.6f}, {2:.6f}".format(
invert_order, slices[1].ImagePositionPatient[2],
slices[0].ImagePositionPatient[2]))
#print("Invert order: ", invert_order, " - ", slices[1].ImagePositionPatient[2], ",", slices[0].ImagePositionPatient[2])
pixel_spacing = slices[0].PixelSpacing
pixel_spacing.append(slices[0].SliceThickness)
image = helpers.rescale_patient_images(image,
pixel_spacing,
settings.TARGET_VOXEL_MM,
verbose=True)
if not invert_order:
image = numpy.flipud(image)
for i in range(image.shape[0]):
patient_dir = target_dir + patient_id + "/"
if not os.path.exists(patient_dir):
os.makedirs(patient_dir)
img_path = patient_dir + "img_" + str(i).rjust(4, '0') + "_i.png"
org_img = image[i]
# if there exists slope,rotation image with corresponding degree
if cos_degree > 0.0:
org_img = cv_flip(org_img, org_img.shape[1], org_img.shape[0],
cos_degree)
img, mask = helpers.get_segmented_lungs(org_img.copy())
org_img = helpers.normalize_hu(org_img)
cv2.imwrite(img_path, org_img * 255)
cv2.imwrite(img_path.replace("_i.png", "_m.png"), mask * 255)
def extract_dicom_images_patient(src_dir):
#directory where the extracted images will go
target_dir = settings.NDSB3_EXTRACTED_IMAGE_DIR
print("Dir: ", src_dir)
#directory of the patient (folder name = patientID)
dir_path = settings.NDSB3_RAW_SRC_DIR + src_dir + "/"
patient_id = src_dir
#sorts slices by acquisition, obtains thickness, applies to all slices, then returns array of slices
slices = load_patient(dir_path)
print(len(slices), "\t", slices[0].SliceThickness, "\t",
slices[0].PixelSpacing)
print("Orientation: ", slices[0].ImageOrientationPatient)
#assert slices[0].ImageOrientationPatient == [1.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000]
cos_value = (slices[0].ImageOrientationPatient[0])
cos_degree = round(math.degrees(math.acos(cos_value)), 2)
#convert to image
pixels = get_pixels_hu(slices)
image = pixels
print(image.shape)
#check if order is inverted
invert_order = slices[1].ImagePositionPatient[2] > slices[
0].ImagePositionPatient[2]
print("Invert order: ", invert_order, " - ",
slices[1].ImagePositionPatient[2], ",",
slices[0].ImagePositionPatient[2])
#rescale images based on pixel spacing
pixel_spacing = slices[0].PixelSpacing
pixel_spacing.append(slices[0].SliceThickness)
image = helpers.rescale_patient_images(image, pixel_spacing,
settings.TARGET_VOXEL_MM)
if not invert_order:
image = numpy.flipud(image)
#go through the height of the image (rows = [0])
for i in range(image.shape[0]):
patient_dir = target_dir + patient_id + "/"
if not os.path.exists(patient_dir):
os.mkdir(patient_dir)
#write image out as .png file
img_path = patient_dir + "img_" + str(i).rjust(4, '0') + "_i.png"
#original image is the one just written to folder
org_img = image[i]
# if there exists slope,rotation image with corresponding degree
if cos_degree > 0.0:
org_img = cv_flip(org_img, org_img.shape[1], org_img.shape[0],
cos_degree)
img, mask = helpers.get_segmented_lungs(org_img.copy())
#normalize hounsfield units
org_img = helpers.normalize_hu(org_img)
cv2.imwrite(img_path, org_img * 255)
cv2.imwrite(img_path.replace("_i.png", "_m.png"), mask * 255)
def preprocess_extradata(start_dir):
#src_dir = settings.BASE_DIR + "extrasets/"
dirs_and_files = os.listdir(start_dir)
dirs = []
dicom_paths = []
for dir_or_file in dirs_and_files:
if os.path.isdir(start_dir + dir_or_file):
dirs.append(start_dir + dir_or_file + "/")
else:
if dir_or_file.endswith(".dcm") or "CT." in dir_or_file:
dicom_paths.append(start_dir + dir_or_file)
# patient_imgs = load_patient(dicom_files)
#patient_pixels = get_pixels_hu(patient_imgs)
for dicom_path in dicom_paths:
try:
dicom_slice = dicom.read_file(dicom_path)
slice_location = 0
slice_location = str(int(round(dicom_slice.SliceLocation * 1000)))
image = dicom_slice.pixel_array
image = image.astype(numpy.int16)
image[image == -2000] = 0
intercept = dicom_slice.RescaleIntercept
slope = dicom_slice.RescaleSlope
if slope != 1:
print("Slope <> 1!!!")
image = slope * image.astype(numpy.float64)
image = image.astype(numpy.int16)
image += numpy.int16(intercept)
image = helpers.normalize_hu(image)
tmp = dicom_path.replace(settings.BASE_DIR + "extradata/", "")
file_name = ntpath.basename(dicom_path)
patient_id = tmp.replace(file_name, "").replace("/", "")
file_name = file_name.replace(".dcm", "")
target_dir = settings.BASE_DIR + "extradata_processed/" + patient_id + "/"
if not os.path.exists(target_dir):
print("Patient: ", patient_id)
os.mkdir(target_dir)
target_path = target_dir + slice_location + "_" + file_name + "_i.png"
cv2.imwrite(target_path, image * 255)
except:
print("EXCEPTION")
for sub_dir in dirs:
preprocess_extradata(sub_dir)
def extract_dicom_images_patient(src_dir):
target_dir = settings.NDSB3_EXTRACTED_IMAGE_DIR
print("Dir: ", src_dir)
dir_path = settings.NDSB3_RAW_SRC_DIR + src_dir + "/"
patient_id = src_dir
slices = load_patient(dir_path)
print(len(slices), "\t", slices[0].SliceThickness, "\t",
slices[0].PixelSpacing)
print("Orientation: ", slices[0].ImageOrientationPatient)
assert slices[0].ImageOrientationPatient == [
1.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000
]
pixels = get_pixels_hu(slices)
image = pixels
print(image.shape)
invert_order = slices[1].ImagePositionPatient[2] > slices[
0].ImagePositionPatient[2]
print("Invert order: ", invert_order, " - ",
slices[1].ImagePositionPatient[2], ",",
slices[0].ImagePositionPatient[2])
pixel_spacing = slices[0].PixelSpacing
pixel_spacing.append(slices[0].SliceThickness)
image = helpers.rescale_patient_images(image, pixel_spacing,
settings.TARGET_VOXEL_MM)
if not invert_order:
image = numpy.flipud(image)
for i in range(image.shape[0]):
patient_dir = target_dir + patient_id + "/"
if not os.path.exists(patient_dir):
os.mkdir(patient_dir)
img_path = patient_dir + "img_" + str(i).rjust(4, '0') + "_i.png"
org_img = image[i]
img, mask = helpers.get_segmented_lungs(org_img.copy())
org_img = helpers.normalize_hu(org_img)
cv2.imwrite(img_path, org_img * 255)
cv2.imwrite(img_path.replace("_i.png", "_m.png"), mask * 255)
