def draw_circles(image, cands, origin, spacing):
# make empty matrix, which will be filled with the mask
image_mask = np.zeros(image.shape)
# run over all the nodules in the lungs
for ca in cands.values:
# get middle x-,y-, and z-worldcoordinate of the nodule
radius = np.ceil(ca[4]) / 2
coord_x = ca[1]
coord_y = ca[2]
coord_z = ca[3]
image_coord = np.array((coord_x, coord_y, coord_z))
# determine voxel coordinate given the worldcoordinate
image_coord = world_2_voxel(image_coord, origin, spacing)
# determine the range of the nodule
noduleRange = seq(-radius, radius, RESIZE_SPACING[0])
# create the mask
for x in noduleRange:
for y in noduleRange:
for z in noduleRange:
coords = world_2_voxel(
np.array((coord_x + x, coord_y + y, coord_z + z)),
origin, spacing)
if (np.linalg.norm(image_coord - coords) *
RESIZE_SPACING[0]) < radius:
image_mask[int(np.round(coords[0])),
int(np.round(coords[1])),
int(np.round(coords[2]))] = int(1)
return image_mask
def draw_circles(image,cands,origin,spacing):
#make empty matrix, which will be filled with the mask
image_mask = np.zeros(image.shape)
#run over all the nodules in the lungs
for ca in cands.values:
#get middel x-,y-, and z-worldcoordinate of the nodule
radius = np.ceil(ca[4])/2
coord_x = ca[1]
coord_y = ca[2]
coord_z = ca[3]
image_coord = np.array((coord_x,coord_y,coord_z))
#determine voxel coordinate given the worldcoordinate
image_coord = world_2_voxel(image_coord,origin,spacing)
#determine the range of the nodule
noduleRange = seq(-radius, radius, RESIZE_SPACING[0])
#create the mask
for x in noduleRange:
for y in noduleRange:
for z in noduleRange:
coords = world_2_voxel(np.array((coord_x+x,coord_y+y,coord_z+z)),origin,spacing)
if (np.linalg.norm(image_coord-coords) * RESIZE_SPACING[0]) < radius:
image_mask[np.round(coords[0]),np.round(coords[1]),np.round(coords[2])] = int(1)
return image_mask
def process_image(image_path, candidates, save_dir):
#load image
image, origin, spacing = load_itk(image_path)
#calculate resize factor
resize_factor = spacing / OUTPUT_SPACING
new_real_shape = image.shape * resize_factor
new_shape = np.round(new_real_shape)
real_resize = new_shape / image.shape
new_spacing = spacing / real_resize
#resize image
image = scipy.ndimage.interpolation.zoom(image, real_resize)
#Pad image with offset (OUTPUT_DIM/2) to prevent problems with candidates on edge of image
offset = OUTPUT_DIM/2
image = np.pad(image,offset,'constant',constant_values=0)
#Make a indixlist of the candidates of the image
image_name = os.path.split(image_path)[1].replace('.mhd','')
indices = candidates[candidates['image_name'] == image_name].index
#loop through the candidates within this image
for i in indices:
#get row data and nodule voxel coords
row = candidates.iloc[i]
world_coords = np.array([row.x, row.y, row.z])
# add offset to voxel coords to cope with padding
coords = np.floor(world_2_voxel(world_coords,origin,new_spacing)) + offset
label = row.label
# Create xy, xz, yz
xy_slice = np.transpose(image[coords[0]-offset:coords[0]+offset,coords[1]-offset:coords[1]+offset,coords[2]])
xz_slice = np.rot90(image[coords[0]-offset:coords[0]+offset,coords[1],coords[2]-offset:coords[2]+offset])
yz_slice = np.rot90(image[coords[0],coords[1]-offset:coords[1]+offset,coords[2]-offset:coords[2]+offset])
# UNCOMMENT THESE LINES IF YOU WANT TO MANUALLY COMPARE IMAGES WITH MEVISLAB
# test_coords means coords you need to look up in mevislab
#test_coords = world_2_voxel(world_coords,origin,spacing)
#print 'x:',test_coords[0],'y:',test_coords[1],'z:',test_coords[2]
#plt.imshow(xy_slice)
#plt.gray()
#plt.figure(2)
#plt.imshow(xz_slice)
#plt.gray()
#plt.figure(3)
#plt.imshow(yz_slice)
#plt.gray()
#plt.show()
# Create output
output = np.zeros([3,OUTPUT_DIM,OUTPUT_DIM])
output[0,:,:] = xy_slice
output[1,:,:] = xz_slice
output[2,:,:] = yz_slice
# Determine save_path based on label and indices (need +2 due to starting with zero + header in csv)
if label:
save_path = save_dir.format('True') + '/{}.pkl.gz'.format(i+2)
else:
save_path = save_dir.format('False') + '/{}.pkl.gz'.format(i+2)
# save with gzip/pickle
with gzip.open(save_path,'wb') as f:
pickle.dump(output,f,protocol=-1)
def process_image(image_path, candidates, save_dir):
# load image
image, origin, spacing = load_itk(image_path)
# calculate resize factor
resize_factor = spacing / OUTPUT_SPACING
new_real_shape = image.shape * resize_factor
new_shape = np.round(new_real_shape)
real_resize = new_shape / image.shape
new_spacing = spacing / real_resize
# resize image
image = scipy.ndimage.interpolation.zoom(image, real_resize)
# Pad image with offset (OUTPUT_DIM/2) to prevent problems with candidates on edge of image
offset = OUTPUT_DIM / 2
image = np.pad(image, offset, 'constant', constant_values=0)
# Make a indixlist of the candidates of the image
image_name = os.path.split(image_path)[1].replace('.mhd', '')
indices = candidates[candidates['image_name'] == image_name].index
# loop through the candidates within this image
for i in indices:
# get row data and nodule voxel coords
row = candidates.iloc[i]
world_coords = np.array([row.x, row.y, row.z])
# add offset to voxel coords to cope with padding
coords = np.floor(world_2_voxel(world_coords, origin,
new_spacing)) + offset
label = row.label
# Create xy, xz, yz
xy_slice = np.transpose(image[coords[0] - offset:coords[0] + offset,
coords[1] - offset:coords[1] + offset,
coords[2]])
xz_slice = np.rot90(image[coords[0] - offset:coords[0] + offset,
coords[1],
coords[2] - offset:coords[2] + offset])
yz_slice = np.rot90(image[coords[0],
coords[1] - offset:coords[1] + offset,
coords[2] - offset:coords[2] + offset])
# UNCOMMENT THESE LINES IF YOU WANT TO MANUALLY COMPARE IMAGES WITH MEVISLAB
# test_coords means coords you need to look up in mevislab
# test_coords = world_2_voxel(world_coords,origin,spacing)
# print 'x:',test_coords[0],'y:',test_coords[1],'z:',test_coords[2]
# plt.imshow(xy_slice)
# plt.gray()
# plt.figure(2)
# plt.imshow(xz_slice)
# plt.gray()
# plt.figure(3)
# plt.imshow(yz_slice)
# plt.gray()
# plt.show()
# Create output
output = np.zeros([3, OUTPUT_DIM, OUTPUT_DIM])
output[0, :, :] = xy_slice
output[1, :, :] = xz_slice
output[2, :, :] = yz_slice
# Determine save_path based on label and indices (need +2 due to starting with zero + header in csv)
if label:
save_path = save_dir.format('True') + '/{}.pkl.gz'.format(i + 2)
else:
save_path = save_dir.format('False') + '/{}.pkl.gz'.format(i + 2)
# save with gzip/pickle
with gzip.open(save_path, 'wb') as f:
pickle.dump(output, f, protocol=-1)