def save_nonnodule(self, nodule_crop, store_name, mhd_store=False):
np.save(
os.path.join(self.nonnodule_npy_path,
store_name + "_nonannotation.npy"), nodule_crop)
if mhd_store:
mt.write_mhd_file(
self.no_annotation_mhd_path + store_name +
"_nonannotation.mhd", nodule_crop, nodule_crop.shape)
nodule_matrix, cindex = cd.candidate_detection(segimage)
cluster_labels = lc.seed_volume_cluster(nodule_matrix,
cluster_size=30,
result_vision=False)
print('Clustering Done')
cluster_labels_merged = lc.cluster_merge(segimage, cluster_labels)
print('Clusters Merged')
lc.view_segment(image, cluster_labels_merged)
candidate_coords = lc.cluster_centers(cluster_labels)
volume_candidated = cv.view_coordinations(image,
candidate_coords,
window_size=10,
reverse=False,
slicewise=True,
show=True)
mt.write_mhd_file(vision_path + "/" + uid + "_candidate.mhd",
volume_candidated, volume_candidated.shape[::-1])
print('Candidate Done')
exit()
window_prehalf = int(WINDOW_SIZE / 2)
window_afterhalf = WINDOW_SIZE - window_prehalf
image_padded = MIN_BOUND * np.ones(
(image.shape[0] + WINDOW_SIZE, image.shape[1] + WINDOW_SIZE,
image.shape[2] + WINDOW_SIZE),
dtype=int)
image_padded[window_prehalf:window_prehalf + image.shape[0],
window_prehalf:window_prehalf + image.shape[1],
window_prehalf:window_prehalf + image.shape[2]] = image
nodule_centers = []
print('candidate number:%d' % (len(candidate_coords)))
for tb in range(0, len(candidate_coords), CANDIDATE_BATCH):
