def main():
if len(sys.argv) > 1:
data_dir = sys.argv[1]
# data_dir = '/hpc/bsha219/lung/Data/Human_PE_Study_HRC/ST12/TLC/Raw/DICOMS'
patient_scans = load_scan(
'/hpc/bsha219/lung/Data/Human_PE_Study_HRC/ST12/TLC/Raw/DICOMS')
patient_images = get_pixels_hu(patient_scans)
global artery_mask
artery_mask = []
for k in range(115, 215):
global coords
coords = []
img = patient_images[k]
root = tk.Tk()
im = Image.fromarray(img)
photo = ImageTk.PhotoImage(im)
canvas = tk.Canvas(root, width=512, height=512)
canvas.pack()
canvas.create_image(256, 256, image=photo)
canvas.bind("<Button-1>", onclick)
root.mainloop()
for i in range(img.shape[0]):
for j in range(img.shape[0]):
if img[i][j] > 0:
img[i][j] *= 12
# blurred_f = ndimage.gaussian_filter(img, sigma=3)
filter_blurred_f = filters.median_filter(img, 3)
filter_blurred_f2 = ndimage.gaussian_filter(filter_blurred_f, sigma=1)
alpha = 12
sharpened = filter_blurred_f + alpha * (filter_blurred_f -
filter_blurred_f2)
seed = np.zeros(img.shape)
for jj in range(len(coords)):
seed[int(coords[jj][1]), int(coords[jj][0])] = 1
seg, phi, its = lvlset(sharpened,
seed,
max_its=1000,
display=True,
alpha=0.4)
segment = seg * 1.0
# scipy.misc.imsave('/hpc/bsha219/Python/Behdad/Out_arterial_mask/LungMask%.4d.jpg' % k, segment) # binary files
matplotlib.image.imsave(
'/hpc/bsha219/Python/Behdad/Out_arterial_mask/' + str(k) + '.png',
segment) # RGB files
def main():
if len(sys.argv) > 1:
data_dir = sys.argv[1]
# data_dir = '/hpc/bsha219/lung/Data/ST12/Raw/DICOMS'
# data_dir = '/hpc/bsha219/lung/Data/P2BRP257-H12076/FRC/Raw/DICOMS'
patient_scans = load_scan('/hpc/bsha219/lung/Data/Human_PE_Study_HRC/ST12/TLC/Raw/DICOMS')
patient_images = get_pixels_hu(patient_scans)
# imgs, spacing = downsample(patient_images, patient_scans, [1, 1, 1])
# segmented = []
for i in range(len(patient_images)):
lungs, left_lung, right_lung = lung_segment(patient_images[i])
# segmented.append(lungs)
# segmented = np.asarray(segmented)
lungs = np.uint8(lungs)
# to save them as a stack of masks in a directory
binary_im = Image.fromarray(lungs)
binary_im.save('/hpc/bsha219/Python/Behdad/Lung_masks/LungMask%.4d.jpg' % i, quality=100)
from load_scan import load_scan, get_pixels_hu
from Lung_Segmentation import lung_segment, generate_markers
import scipy.ndimage as ndimage
import matplotlib.pyplot as plt
import matplotlib.image as im
ct_images = load_scan('../examples/sample_inputs')
images_hu = get_pixels_hu(ct_images)
for i in range(len(images_hu)):
marker_internal, marker_watershed = generate_markers(images_hu[i])
marker_internal[marker_internal == 2] = 1
lungs, lung_left, lung_right = lung_segment(images_hu[i])
lungs[lungs == 17] = 1
lungs[lungs == 5] = 1
ex = lungs - marker_internal * 1.0
vessel = ndimage.binary_opening(ex)
plt.imshow(vessel)
plt.show()
im.imsave('./results/Vessel' + str(i + 1) + '.jpg', vessel)
from load_scan import load_scan, get_pixels_hu
from Lung_Segmentation import lung_segment, generate_markers
import scipy.ndimage as ndimage
import matplotlib
import matplotlib.pyplot as plt
patient_scans = load_scan('/hpc/bsha219/lung/Data/ST12/Raw/DICOMS')
patient_images = get_pixels_hu(patient_scans)
for i in range(len(patient_images)):
marker_internal, marker_external, marker_watershed, left_lung, right_lung, ex_left, ex_right, watershed_left, watershed_right = generate_markers(
patient_images[i])
seg, watershed, mark_int, mark_ext, mark_watershed, wat_left, wat_right = lung_segment(
patient_images[i])
watershed[watershed == 128] = 0
watershed[watershed == 255] = 1
ex = watershed - marker_internal * 1.0
vessel = ndimage.binary_opening(ex)
# matplotlib.image.imsave('/hpc/bsha219/Python/Behdad/Inside_vessels/' + str(i) + '.png', vessel)
# plt.imshow(vessel, cmap='gray')
# plt.show()