基于深度学习的CT图像肺结节自动检测技术一——数据预处理（归一化，数据增强，数据标记）

文章目录

* 一、读取图片dicom —>截取像素 —>归—化(0,1) —>存储为png
<https://blog.csdn.net/wsp_1138886114/article/details/81872555#dicom__01_png_10>
* 二、图像增强：（旋转，平移）
<https://blog.csdn.net/wsp_1138886114/article/details/81872555#_96>
* 三、准备U-net训练数据—数据标记
<https://blog.csdn.net/wsp_1138886114/article/details/81872555#Unet_162>

开发环境 Anaconda:jupyter notebook /pycharm pip install SimpleItk # 读取CT医学图像 pip
install tqdm# 可扩展的Python进度条，封装迭代器 pip install pydicom # 用于读取 dicom 图片 pip
install opencv-python
<>一、读取图片dicom —>截取像素 —>归—化(0,1) —>存储为png
""" 读取dicom图像并将其转换为png图像 读取某文件夹内的所有dicom文件 :param src_dir: dicom文件夹路径 :return:
dicom list """ import os import SimpleITK import dicom import numpy as np import
cv2from tqdm import tqdm def is_dicom_file(filename): #判断某文件是否是dicom格式的文件
file_stream= open(filename, 'rb') file_stream.seek(128) data = file_stream.read(
4) file_stream.close() if data == b'DICM': return True return False def
load_patient(src_dir): #读取某文件夹内的所有dicom文件 files = os.listdir(src_dir) slices = [
] for s in files: if is_dicom_file(src_dir + '/' + s): instance = dicom.
read_file(src_dir + '/' + s) slices.append(instance) slices.sort(key=lambda x:
int(x.InstanceNumber)) try: slice_thickness = np.abs(slices[0].
ImagePositionPatient[2]\ - slices[1].ImagePositionPatient[2]) except:
slice_thickness= np.abs(slices[0].SliceLocation - slices[1].SliceLocation) for s
in slices: s.SliceThickness = slice_thickness return slices def
get_pixels_hu_by_simpleitk(dicom_dir): #读取某文件夹内的所有dicom文件,并提取像素值(-4000 ~ 4000)
reader= SimpleITK.ImageSeriesReader() dicom_names = reader.
GetGDCMSeriesFileNames(dicom_dir) reader.SetFileNames(dicom_names) image =
reader.Execute() img_array = SimpleITK.GetArrayFromImage(image) img_array[
img_array== -2000] = 0 return img_array def normalize_hu(image):
#将输入图像的像素值(-4000 ~ 4000)归一化到0~1之间 MIN_BOUND = -1000.0 MAX_BOUND = 400.0 image =
(image - MIN_BOUND) / (MAX_BOUND - MIN_BOUND) image[image > 1] = 1. image[image
< 0] = 0. return image if __name__ == '__main__': dicom_dir =
'./data/dicom_demo/' # 读取dicom文件的元数据(dicom tags) slices = load_patient(dicom_dir
) print('The number of dicom files : ', len(slices)) image =
get_pixels_hu_by_simpleitk(dicom_dir) # 提取dicom文件中的像素值 for i in tqdm(range(image
.shape[0])): img_path = "./temp_dir/dcm_2_png/img_" + str(i).rjust(4, '0') +
"_i.png" org_img = normalize_hu(image[i]) # 将像素值归一化到[0,1]区间 cv2.imwrite(img_path
, org_img * 255) # 保存图像数组为灰度图(.png)
<>二、图像增强：（旋转，平移）
""" 图像数据增强(augmentation)操作 """ import random import numpy as np import cv2 def
random_rotate_img(img, min_angle, max_angle): """ 图像旋转 random rotation an image
:param img: image to be rotated :param min_angle: min angle to rotate :param
max_angle: max angle to rotate :return: image after random rotated """ if not
isinstance(img, list): img = [img] angle = random.randint(min_angle, max_angle)
center= (img[0].shape[0] / 2, img[0].shape[1] / 2) rot_matrix = cv2.
getRotationMatrix2D(center, angle, scale=1.0) res = [] for img_inst in img:
img_inst= cv2.warpAffine(img_inst, rot_matrix,dsize=img_inst.shape[:2],
borderMode=cv2.BORDER_CONSTANT) res.append(img_inst) if len(res) == 0: res = res
[0] return res def random_flip_img(img): ''' 图像平移 random flip image,both on
horizontal and vertical :param img: image to be flipped :return: image after
flipped ''' flip_val = 0 if not isinstance(img, list): res = cv2.flip(img,
flip_val) # 0 = X axis, 1 = Y axis, -1 = both else: res = [] for img_item in img
: img_flip = cv2.flip(img_item, flip_val) res.append(img_flip) return res if
__name__== '__main__': imagePath = './data/chaper3_img_01.png' #
读取dicom文件的元数据(dicom tags) img = cv2.imread(imagePath, cv2.IMREAD_GRAYSCALE)
print(img.shape) res_rotate = random_rotate_img(img, 30, 90) cv2.imwrite(
'./temp_dir/chapter3_rotate_img.png', res_rotate[0]) res_flip = random_flip_img(
img) cv2.imwrite('./temp_dir/chapter3_flip_img.png', res_flip)
<>三、准备U-net训练数据—数据标记
# 准备U-net训练数据 from scipy import ndimage as ndi import numpy import cv2
MASK_MARGIN= 5 def make_mask(v_center, v_diam, width, height): mask = numpy.
zeros([height, width]) v_xmin = numpy.max([0, int(v_center[0] - v_diam) -
MASK_MARGIN]) v_xmax = numpy.min([width - 1, int(v_center[0] + v_diam) +
MASK_MARGIN]) v_ymin = numpy.max([0, int(v_center[1] - v_diam) - MASK_MARGIN])
v_ymax= numpy.min([height - 1, int(v_center[1] + v_diam) + MASK_MARGIN])
v_xrange= range(v_xmin, v_xmax + 1) v_yrange = range(v_ymin, v_ymax + 1) for v_x
in v_xrange: for v_y in v_yrange: p_x = v_x p_y = v_y if numpy.linalg.norm(numpy
.array([v_center[0], v_center[1]])\ - numpy.array([p_x, p_y]))<= v_diam * 2:
mask[p_y, p_x] = 1.0 #设置节点区域的像素值为1 return (mask) if __name__ == '__main__':
imagePath= './data/chaper3_img_01.png' # 读取dicom文件的元数据(dicom tags) img = cv2.
imread(imagePath, cv2.IMREAD_GRAYSCALE) print('before resize: ', img.shape)
img_X= ndi.interpolation.zoom(img, [320/512, 320/512], mode='nearest') print(
'before resize: ', img_X.shape) cv2.imwrite('./temp_dir/chapter3_img_X.png',
img_X) img_Y = make_mask((217, 160), 3, 320, 320) img_Y[img_Y < 0.5] = 0 img_Y[
img_Y> 0.5] = 255 nodule_mask = img_Y.astype('uint8') cv2.imwrite(
'./temp_dir/chapter3_img_Y.png', img_Y)

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