def preprocessing_seg(self, label_volume_data_loc):
image_volume_data = self.__image_volume_data
first_dataset_item = self.__list_dataset_image[0]
pid = os.path.basename(self.__one_case_path)
## 1-label_loc定位图像处理
# 1-对label_loc定位图像重采样
label_volume_data_loc_resampled = image_resample(
first_dataset_item, label_volume_data_loc, g_slice_thickness)
# 2-对于label_loc定位图像，如果宽高不相等，则调整为相等
label_volume_data_loc_shape_fixed = processor.image_padding(
label_volume_data_loc_resampled,
label_volume_data_loc_resampled.shape[1],
label_volume_data_loc_resampled.shape[2])
## 2-根据label_loc定位图像对image数据处理
# 2.1-重采样，默认层厚3mm
image_volume_data_resampled = image_resample(first_dataset_item,
image_volume_data,
g_slice_thickness)
self.__resampled_shape = image_volume_data_resampled.shape
# 2.2-设置window
image_volume_data_wwwl = set_ww_wl(image_volume_data_resampled,
self.__roi_name, self.__modality,
pid, self.__sos_format)
# 2.3-如果image数据长和宽不相等则调整为相等，否则不作处理
image_volume_data_shape_fixed = processor.image_padding(
image_volume_data_wwwl, image_volume_data_wwwl.shape[1],
image_volume_data_wwwl.shape[2])
self.__padded_shape = image_volume_data_shape_fixed.shape
# 2.4-根据label_loc定位图像对image数据进行抠图
if self.__roi_name == 'Len_L' or self.__roi_name == 'Len_R' or self.__roi_name == 'Optic_Nerve_L' or self.__roi_name == 'Optic_Nerve_R':
box = processor.get_bounding_box(label_volume_data_loc_shape_fixed,
self.__predifined_box_size[0],
xy_padding=g_xy_padding,
z_padding=4)
else:
box = processor.get_bounding_box(label_volume_data_loc_shape_fixed,
self.__predifined_box_size[0])
self.__crop_box = box
image_volume_data_box = np.zeros(
[self.__predifined_box_size[0], box[3] - box[2], box[5] - box[4]],
np.uint8)
image_volume_data_box[0:box[1] -
box[0]] = image_volume_data_shape_fixed[
box[0]:box[1], box[2]:box[3], box[4]:box[5]]
# save_images(image_volume_data_box, pid, '03.image', os.path.join(g_tmp_dir_base, self.__roi_name))
# 2.5-根据超参（如bbox=[96, 128, 128]）进行缩放
image_volume_data_resized = processor.image_resize_3d(
image_volume_data_box, self.__predifined_box_size[1],
self.__predifined_box_size[2])
save_images(image_volume_data_resized, pid, '795',
os.path.join(g_tmp_dir_base, self.__roi_name))
return image_volume_data_resized
def preprocessing_loc(self, pid):
'''
stage A过程的预处理
:param pid:
:return:
'''
image_volume = self.__image_volume_data
self.__org_volume_shape = self.__image_volume_data.shape
# 1-重采样，默认层厚3mm
first_dataset_item = self.__list_dataset_image[0]
image_volume = image_resample(first_dataset_item, image_volume,
g_slice_thickness)
self.__resampled_shape = image_volume.shape
# 2-设置window
image_volume = set_ww_wl(image_volume, self.__roi_name,
self.__modality, pid, self.__sol_format)
save_images(image_volume, pid, '666',
os.path.join(g_tmp_dir_base, self.__roi_name))
# 3-如果image数据长和宽不相等则调整为相等，否则不做处理
shape = image_volume.shape
image_volume = processor.image_padding(image_volume, shape[1],
shape[2])
self.__padded_shape = image_volume.shape
# save_images(image_volume, pid, '02.image', os.path.join(g_tmp_dir_base, self.__roi_name))
# 4-根据超参数缩放图像
result_image_volume_data = processor.image_resize_3d(
image_volume, self.__height, self.__width)
# save_images(image_volume, pid, '03.image', os.path.join(g_tmp_dir_base, self.__roi_name))
return result_image_volume_data
def preprocessing(self, image_volume_data, pid):
# 获取第一个slice的dicom信息，例如：spacing、orgin、thickness等参数，后面重采样使用
first_dataset_item = self.__dict_all_case_info[pid].first_dataset_item
# 1-图像重采样，层厚设置为默认3mm
image_volume_data_resampled = image_resample(first_dataset_item,
image_volume_data,
g_slice_thickness)
# 2-设置窗宽窗位
image_volume_data_wwwl = set_ww_wl(image_volume_data_resampled,
self.__roi_name, self.__modality,
pid, self.__format)
# 3-如果输入图像宽和高不相等，则变换为相等，如：512x512 --> 512x512; 192x256 --> 256x256; 1024x512 --> 1024x1024
image_volume_data_padded = processor.image_padding(
image_volume_data_wwwl, image_volume_data_wwwl.shape[1],
image_volume_data_wwwl.shape[2])
# save_images(image_volume_data_padded, pid, '02.image')
# 4-缩放图像至指定的大小，大小为超参数，人为指定，例如：
# 'Spinal_Cord': Parameter(... depth=64, height=96, width=96
image_volume_data_resized = processor.image_resize_3d(
image_volume_data_padded, self.__height, self.__width)
result_image_volume_data = image_volume_data_resized
return result_image_volume_data
def preprocessing(self, image_volume_data, label_volume_data):
'''
对一个病例的image和label进行数据预处理
:param image_volume_data:
:param label_volume_data:
:param pid:
:return:
'''
# 1-如果输入图像宽和高不相等，则变换为相等，如：512x512 --> 512x512; 192x256 --> 256x256; 1024x512 --> 1024x1024
shape = image_volume_data.shape
image_volume = processor.image_padding(image_volume_data, shape[1],
shape[2])
# 2-缩放图像至指定的大小，大小为超参数，人为指定，例如：
# 'Spinal_Cord': Parameter(... depth=64, height=96, width=96
image_volume = processor.image_resize_3d(image_volume, self.__height,
self.__width)
# 3-如果输入label图像宽和高不相等，则变换为相等，如：512x512 --> 512x512; 192x256 --> 256x256; 1024x512 --> 1024x1024
shape = label_volume_data.shape
label_volume = processor.image_padding(label_volume_data, shape[1],
shape[2])
# 4-缩放label图像至指定的大小，大小为超参数，人为指定，例如：
# 'Spinal_Cord': Parameter(... depth=64, height=96, width=96
label_volume = processor.image_resize_3d(label_volume, self.__height,
self.__width)
# 5-对label图像进行二值化阈值处理，小于127设为0，大于127设为255
label_volume = processor.image_threshold_binaray_3d(label_volume, 127)
# save_images(label_volume_data_dilated, pid, '03.label')
# 6-将label图像规范化处理：[0 ~ 255] --> [0 ~ 1]
label_volume = processor.normalize_image_data(label_volume)
return image_volume, label_volume
def preprocessing(self, pid, image_volume_data, label_volume_data,
label_volume_data_loc):
first_dataset_item = self.__dict_all_case_info[pid].first_dataset_item
## 1-label_loc
# 1.1-对lable_loc定位图像进行重采样，默认层厚3mm
label_volume_data_loc_resampled = image_resample(
first_dataset_item, label_volume_data_loc, g_slice_thickness)
# 1.2-对lable_loc定位图像进行宽和高调整为相等
label_volume_data_loc_shape_fixed = processor.image_padding(
label_volume_data_loc_resampled,
label_volume_data_loc_resampled.shape[1],
label_volume_data_loc_resampled.shape[2])
# 1.3-根据label_loc定位图像获取boundingbox
if self.__roi_name == 'Len_L' or self.__roi_name == 'Len_R' or \
self.__roi_name == 'Optic_Nerve_L' or self.__roi_name == 'Optic_Nerve_R' or self.__roi_name == 'Pituitary':
box = processor.get_bounding_box(label_volume_data_loc_shape_fixed,
self.__depth,
xy_padding=g_xy_padding,
z_padding=4)
else:
box = processor.get_bounding_box(label_volume_data_loc_shape_fixed,
self.__depth)
self.__dict_all_case_info[pid].box = box
## 2-image
# 2.1-对image数据进行重采样，默认层厚为3mm
image_volume_data_resampled = image_resample(first_dataset_item,
image_volume_data,
g_slice_thickness)
# 对重采样之后的image数据设置window
image_volume_data_wwwl = set_ww_wl(image_volume_data_resampled,
self.__roi_name, self.__modality,
pid, self.__sos_format)
# save_images(image_volume_data_wwwl, pid, '02image')
# 2.2-对image数据进行宽和高调整为相等
image_volume_data_shape_fixed = processor.image_padding(
image_volume_data_wwwl, image_volume_data_wwwl.shape[1],
image_volume_data_wwwl.shape[2])
# 2.3-根据boudingbox抠图，注意：get_bounding_box返回的图像宽和高总是相等的
image_volume_data_box = np.zeros(
[self.__depth, box[3] - box[2], box[5] - box[4]], np.uint8)
image_volume_data_box[0:box[1] -
box[0]] = image_volume_data_shape_fixed[
box[0]:box[1], box[2]:box[3], box[4]:box[5]]
# save_images(image_volume_data_box, pid, '03image')
# 2.4-由于get_bounding_box返回的图像宽和高总是相等的，所以将抠图之后的image bbox data直接进行缩放
image_volume_data_resized = processor.image_resize_3d(
image_volume_data_box, self.__height, self.__width)
save_images(image_volume_data_resized, pid, 'image',
os.path.join(g_tmp_dir_base, self.__roi_name))
result_image_volume_data = image_volume_data_resized
# 为了观察理解，可以将预处理之后的图像保存
# save_images(result_image_volume_data, pid, 'image_bbox')
## 3-label
# 3.1-对label数据进行重采样
label_volume_data_resampled = image_resample(first_dataset_item,
label_volume_data,
g_slice_thickness)
# save_images(label_volume_data_resampled, pid, '02label')
# 3.2-对label数据进行宽和高调整为相等
label_volume_data_shape_fixed = processor.image_padding(
label_volume_data_resampled, label_volume_data_resampled.shape[1],
label_volume_data_resampled.shape[2])
# 3.3-根据boudingbox抠图，注意：get_bounding_box返回的图像宽和高总是相等的
label_volume_data_box = np.zeros(
[self.__depth, box[3] - box[2], box[5] - box[4]], np.uint8)
label_volume_data_box[0:box[1] -
box[0]] = label_volume_data_shape_fixed[
box[0]:box[1], box[2]:box[3], box[4]:box[5]]
# 3.4-由于get_bounding_box返回的图像宽和高总是相等的，所以将抠图之后的image bbox data直接进行缩放
label_volume_data_resized = processor.image_resize_3d(
label_volume_data_box, self.__height, self.__width)
save_images(label_volume_data_resized, pid, 'label',
os.path.join(g_tmp_dir_base, self.__roi_name))
# 为了观察理解，可以将预处理之后的图像保存
# save_images(label_volume_data_resized, pid, 'label_bbox')
# 对label数据归一化到0~1区间
result_label_volume_data = processor.normalize_image_data(
label_volume_data_resized)
return result_image_volume_data, result_label_volume_data