以下方法正是这样做的，获取多个水平切片并将它们放入 2D 矩阵
def slices_matrix_2D(img):
''' Transform a 3D MRI image into a 2D image, by obtaining 9 slices
and placing them in a 4x4 two-dimensional grid.
All 16 cuts are from a horizontal/axial view. They are selected
from the 30th to the 60th level of the original 3D image.
Parameters:
img -- np.ndarray with the 3D image
Returns:
np.ndarray -- The resulting 2D image
'''
# create the final 2D image
image_2D = np.empty(IMG_2D_SHAPE)
# set the limits and the step
TOP = 60
BOTTOM = 30
STEP = 2
N_CUTS = 16
# iterator for the cuts
cut_it = TOP
# iterator for the rows of the 2D final image
row_it = 0
# iterator for the columns of the 2D final image
col_it = 0
for cutting_time in range(N_CUTS):
# cut
cut = img[cut_it, :, :]
cut_it -= STEP
# reset the row iterator and move the
# col iterator when needed
if cutting_time in [4, 8, 12]:
row_it = 0
col_it += cut.shape[1]
# copy the cut to the 2D image
for i in range(cut.shape[0]):
for j in range(cut.shape[1]):
image_2D[i + row_it, j + col_it] = cut[i, j]
row_it += cut.shape[0]
# return the final 2D image, with 3 channels
# this is necessary for working with most pre-trained nets
return np.repeat(image_2D[None, ...], 3, axis=0).T
#return image_2D
**以下方法使用之前的 2D 变换从磁盘加载 3D 图像并对其进行变换。还返回图像标签。**
def load_image_2D(abs_path, labels):
''' Load an image (.nii) and its label, from its absolute path.
Transform it into a 2D image, by obtaining 16 slices and placing them
in a 4x4 two-dimensional grid.
Parameters:
abs_path -- Absolute path, filename included
labels -- Label mapper
Returns:
img -- The .nii image, converted into a numpy array
label -- The label of the image (from argument 'labels')
'''
# obtain the label from the path (it is the last directory name)
label = labels[abs_path.split('/')[-2]]
# load the image with SimpleITK
sitk_image = sitk.ReadImage(abs_path)
# transform into a numpy array
img = sitk.GetArrayFromImage(sitk_image)
# apply whitening
img = preprocessing.whitening(img)
# make the 2D image
img = slices_matrix_2D(img)
return img, label
为每个 .tfrecord 定义完整的文件名。
train_tfrec2D = os.path.join(DB_TF_2D_PATH, TFREC_2D_SS_TRAIN)
test_tfrec2D = os.path.join(DB_TF_2D_PATH, TFREC_2D_SS_TEST)
val_tfrec2D = os.path.join(DB_TF_2D_PATH, TFREC_2D_SS_VAL)
给定所有图像文件名、文件名和标签映射器（始终为 LABELS 常量），设计创建 .tfrecords 的方法。它使用前两种方法来加载图像
def create_tf_record_2D(img_filenames, tf_rec_filename, labels):
''' Create a TFRecord file, including the information
of the specified images, after converting them into
a 2D grid.
Parameters:
img_filenames -- Array with the path to every
image that is going to be included
in the TFRecords file.
tf_rec_filename -- Name of the TFRecords file.
labels -- Label mapper
'''
# open the file
writer = tf.python_io.TFRecordWriter(tf_rec_filename)
# iterate through all .nii files
for meta_data in img_filenames:
# load the image and label
img, label = load_image_2D(meta_data, labels)
# create a feature
feature = {'label': _int64_feature(label),
'image': _float_feature(img.ravel())}
# create an example protocol buffer
example = tf.train.Example(features=tf.train.Features(feature=feature))
# serialize to string and write on the file
writer.write(example.SerializeToString())
writer.close()
最后，创建 .tfrecords。
create_tf_record_2D(training_set, train_tfrec2D, LABELS)
create_tf_record_2D(test_set, test_tfrec2D, LABELS)
create_tf_record_2D(validation_set, val_tfrec2D, LABELS)