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 process_image(src_path):
patient_id = src_path[-14:-4]
print("Patient: ", patient_id)
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
#print("Img array: ", img_array.shape)
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
#print("Origin (x,y,z): ", origin)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
#print("Direction: ", direction)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
#print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
#print("Rescale: ", rescale)
img_array = helpers.rescale_patient_images(img_array, spacing, settings.TARGET_VOXEL_MM)
img_list = []
for i in range(img_array.shape[0]):
img = img_array[i]
seg_img, mask = helpers.get_segmented_lungs(img.copy())
img_list.append(seg_img)
img = normalize(img)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png", img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_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 process_image(src_path):
"""Load the '.mhd' file, extract the 3D numpy array, rescale the data,
write out each slice as '.png' and each segmented mask as '.png'
"""
patient_id = ntpath.basename(src_path).replace(
".mhd", "") #extract patient id from filename
print("Patient: ", patient_id)
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
origin = numpy.array(
itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
direction = numpy.array(
itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
spacing = numpy.array(
itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
img_array = helpers.rescale_patient_images(img_array, spacing,
settings.TARGET_VOXEL_MM)
img_list = []
for i in range(img_array.shape[0]):
img = img_array[i]
seg_img, mask = helpers.get_segmented_lungs(
img.copy()) #find the segmented image and the mask
img_list.append(seg_img)
img = normalize(img)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png",
img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png",
mask * 255)
def process_image(src_path):
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print("Patient: ", patient_id)
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
origin = numpy.array(
itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
direction = numpy.array(
itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
spacing = numpy.array(
itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
#Rescale our scan
img_array = helpers.rescale_patient_images(img_array, spacing,
settings.TARGET_VOXEL_MM)
img_list = []
#Iterate on each frame
for i in range(img_array.shape[0]):
img = img_array[i]
#Get the segmented lungs, so we know the regions of interest, using Segmentation
seg_img, mask = helpers.get_segmented_lungs(img.copy())
img_list.append(seg_img)
img = normalize(img)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png",
img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png",
mask * 255)
def process_image(src_path):
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print("Patient: ", patient_id)
if patient_id == 'LKDS-00384':
return
dst_dir = settings.TEST_EXTRACTED_IMAGE_DIR + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
origin = numpy.array(
itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
direction = numpy.array(
itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
spacing = numpy.array(
itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
#归一化，体素间距为1，z保留512个最多
img_array = helpers.rescale_patient_images(img_array, spacing,
settings.TARGET_VOXEL_MM)
img_list = []
for i in range(img_array.shape[0]):
img = img_array[i]
seg_img, mask = helpers.get_segmented_lungs(img.copy())
img_list.append(seg_img)
img = normalize(img)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png",
img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png",
mask * 255)
def process_image(src_path):
patient_id = os.path.basename(src_path).replace(".mhd", "")
settings.log.info("Patient: {0}".format(patient_id))
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
settings.log.info("Img array: {0}".format(img_array.shape))
origin = numpy.array(
itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
settings.log.info("Origin (x,y,z): {0}".format(origin))
direction = numpy.array(
itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
settings.log.info("Direction: {0}".format(direction))
spacing = numpy.array(
itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
settings.log.info("Spacing (x,y,z): {0}".format(spacing))
rescale = spacing / settings.TARGET_VOXEL_MM
settings.log.info("Rescale: {0}".format(rescale))
img_array = helpers.rescale_patient_images(img_array, spacing,
settings.TARGET_VOXEL_MM)
img_list = []
for i in range(img_array.shape[0]):
img = img_array[i]
seg_img, mask = helpers.get_segmented_lungs(img.copy())
img_list.append(seg_img)
img = normalize(img)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png",
img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png",
mask * 255)
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 process_image(src_path):
patient_id = ntpath.basename(src_path).replace(".mhd", "")
#df_patient = annotations[annotations['seriesuid'] == patient_id]
#print("Patient: ", patient_id)
dst_dir = traindata_path + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
origin = numpy.array(
itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
direction = numpy.array(
itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
spacing = numpy.array(
itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / setting.TARGET_VOXEL_MM
print("Rescale: ", rescale)
#calc real origin
flip_direction_x = False
flip_direction_y = False
if round(direction[0]) == -1:
origin[0] *= -1
direction[0] = 1
flip_direction_x = True
print("Swappint x origin")
if round(direction[4]) == -1:
origin[1] *= -1
direction[4] = 1
flip_direction_y = True
print("Swappint y origin")
print("Direction: ", direction)
assert abs(sum(direction) - 3) < 0.01
try:
img_array = helpers.rescale_patient_images(img_array, spacing,
setting.TARGET_VOXEL_MM)
except:
print("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa")
img_list = []
lung_mask = lung_segmentation.segment_HU_scan_elias(img_array)
for i in range(img_array.shape[0]):
#img = img_array[i]
#img = normalize(img)
#mask = lung_mask[i]
#cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png", img * 255)
#cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png", mask * 255)
#orgin kaggle ranking 2 code
#nodule_mask = numpy.zeros([512,512])
img = img_array[i]
seg_img, mask = helpers.get_segmented_lungs(img.copy())
img_list.append(seg_img)
img = normalize(img)
#img = normalize(img)
#img[mask==0] = 0
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png",
img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png",
mask * 255)
