def load_image(img_name, centers):
''' loads a single image and returns a list of subimages (cubes around nodule-like
findings)
img_name: valid path to an image from current working directory
centers: a list of the centroids of nodule-like occurrences in img
in world coordinates
'''
scan, spacing, origin, transfmat = readMhd(img_name)
transfmat_toimg, _ = getImgWorldTransfMats(spacing,
transfmat) # just need image
centers = [convertToImgCoord(c, origin, transfmat_toimg) for c in centers]
subimages = [extractCube(scan, spacing, c) for c in centers]
return subimages
vol = float(n[header.index('Volume')])
if nodEqDiam(vol)>3: #only get nodule cubes for nodules>3mm
ctr = np.array([float(n[header.index('x')]), float(n[header.index('y')]), float(n[header.index('z')])])
lnd = int(n[header.index('LNDbID')])
rad = int(n[header.index('RadID')])
# rads = list(map(int,list(n[header.index('RadID')].split(','))))
# radfindings = list(map(int,list(n[header.index('RadFindingID')].split(','))))
finding = int(n[header.index('FindingID')])
# print(lnd,finding,rads,radfindings)
print(lnd,finding,rad,finding)
# Read scan
if lnd!=lndloaded:
[scan,spacing,origin,transfmat] = readMhd('/media/tungthanhlee/SSD/grand_challenge/dataset/LNDB_segmentation/data/LNDb-{:04}.mhd'.format(lnd))
transfmat_toimg,transfmat_toworld = getImgWorldTransfMats(spacing,transfmat)
lndloaded = lnd
# Convert coordinates to image
ctr = convertToImgCoord(ctr,origin,transfmat_toimg)
# for rad,radfinding in zip(rads,radfindings):
# Read segmentation mask
[mask,_,_,_] = readMhd('/media/tungthanhlee/SSD/grand_challenge/dataset/LNDB_segmentation/masks/LNDb-{:04}_rad{}.mhd'.format(lnd,rad))
# Extract cube around nodule
scan_cube = extractCube(scan,spacing,ctr)
masknod = copy.copy(mask)
# masknod[masknod!=radfinding] = 0
masknod[masknod!=finding] = 0
# ## 3 adjacent grayscale axial slides
if not os.path.isdir('sliceTs'):
os.mkdir('sliceTs')
tsFiles = []
t_wlds = []
origins = []
id_c = 1
id_n = 0
input_shapes = []
for index in tqdm(range(len(filepaths_imgs))):
sc, sp, o, t = utils.readMhd(filepaths_imgs[index])
t_img, t_wld = utils.getImgWorldTransfMats(sp, t)
z_max = sc.shape[0]
z_max_idx = z_max - (sc.shape[0] % 3)
for id_n in range(int(z_max_idx * .6), int(z_max_idx * .9), 3):
origins.append(o)
input_shapes.append(sc.shape)
slide = sc[int(id_n) - 1:int(id_n) + 2, :, :]
slide = equalize_hist(slide)
slide = anisotropic_diffusion(slide,
voxelspacing=sp,
kappa=20,
gamma=0.01,
niter=100,
option=2)