这篇教程基于深度学习的CT图像肺结节自动检测技术二——训练数据处理写得很实用，希望能帮到您。
一、训练数据处理
读取dicom 图片—提取4000mm像素
将dicom图像归一化（1mm*3的尺度）
切割为小cube并平铺存储为png
4000mm像素值归一化—>读取图像：3维cube图像（保存为2维图像方便查看）
开发环境 Anaconda:jupyter notebook/pycharm
pip install dicom # 用于读取 dicom 图片(version0.9.9)
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、训练数据处理
读取dicom 图片—提取4000mm像素
# 准备3dcnn的训练数据
import os
import SimpleITK
import dicom
import numpy as np
import cv2
import glob
from tqdm import tqdm
def is_dicom_file(filename):
"""
判断某文件是否是dicom格式的文件
:param filename: dicom文件的路径
:return:
"""
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
将dicom图像归一化（1mm*3的尺度）
def rescale_patient_images(images_zyx, org_spacing_xyz, target_voxel_mm, is_mask_image=False):
"""
将dicom图像缩放到1mm:1mm:1mm的尺度
:param images_zyx: 缩放前的图像(3维)
:return: 缩放后的图像(3维)
"""
print("Spacing: ", org_spacing_xyz)
print("Shape: ", images_zyx.shape)
# print ("Resizing dim z")
resize_x = 1.0
resize_y = float(org_spacing_xyz[2]) / float(target_voxel_mm)
interpolation = cv2.INTER_NEAREST if is_mask_image else cv2.INTER_LINEAR
res = cv2.resize(images_zyx, dsize=None, fx=resize_x, fy=resize_y, interpolation=interpolation)
# print ("Shape is now : ", res.shape)
res = res.swapaxes(0, 2)
res = res.swapaxes(0, 1)
# print ("Shape: ", res.shape)
resize_x = float(org_spacing_xyz[0]) / float(target_voxel_mm)
resize_y = float(org_spacing_xyz[1]) / float(target_voxel_mm)
# cv2 can handle max 512 channels..
if res.shape[2] > 512:
res = res.swapaxes(0, 2)
res1 = res[:256]
res2 = res[256:]
res1 = res1.swapaxes(0, 2)
res2 = res2.swapaxes(0, 2)
res1 = cv2.resize(res1, dsize=None, fx=resize_x, fy=resize_y, interpolation=interpolation)
res2 = cv2.resize(res2, dsize=None, fx=resize_x, fy=resize_y, interpolation=interpolation)
res1 = res1.swapaxes(0, 2)
res2 = res2.swapaxes(0, 2)
res = np.vstack([res1, res2])
res = res.swapaxes(0, 2)
else:
res = cv2.resize(res, dsize=None, fx=resize_x, fy=resize_y, interpolation=interpolation)
res = res.swapaxes(0, 2)
res = res.swapaxes(2, 1)
print("Shape after: ", res.shape)
return res
切割为小cube并平铺存储为png
def get_cube_from_img(img3d, center_x, center_y, center_z, block_size):
#切割为小cube并平铺存储为png
start_x = max(center_x - block_size / 2, 0)
if start_x + block_size > img3d.shape[2]:
start_x = img3d.shape[2] - block_size
start_y = max(center_y - block_size / 2, 0)
start_z = max(center_z - block_size / 2, 0)
if start_z + block_size > img3d.shape[0]:
start_z = img3d.shape[0] - block_size
start_z = int(start_z)
start_y = int(start_y)
start_x = int(start_x)
res = img3d[start_z:start_z + block_size,
start_y:start_y + block_size,
start_x:start_x + block_size]
return res
4000mm像素值归一化—>读取图像：3维cube图像（保存为2维图像方便查看）
def normalize_hu(image):
"""
将输入图像的像素值(-4000 ~ 4000)归一化到0~1之间
:param image 输入的图像数组
:return: 归一化处理后的图像数组
"""
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
def load_patient_images(src_dir, wildcard="*.*", exclude_wildcards=[]):
"""
读取一个病例的所有png图像，返回值为一个三维图像数组
:param image 输入的一系列png图像
:return: 三维图像数组
"""
src_img_paths = glob.glob(src_dir + wildcard)
for exclude_wildcard in exclude_wildcards:
exclude_img_paths = glob.glob(src_dir + exclude_wildcard)
src_img_paths = [im for im in src_img_paths if im not in exclude_img_paths]
src_img_paths.sort()
images = [cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) for img_path in src_img_paths]
images = [im.reshape((1,) + im.shape) for im in images]
res = np.vstack(images)
return res
def save_cube_img(target_path, cube_img, rows, cols):
"""
将3维cube图像保存为2维图像,方便勘误检查
:param 二维图像保存路径, 三维输入图像
:return: 二维图像
"""
assert rows * cols == cube_img.shape[0]
img_height = cube_img.shape[1]
img_width = cube_img.shape[1]
res_img = np.zeros((rows * img_height, cols * img_width), dtype=np.uint8)
for row in range(rows):
for col in range(cols):
target_y = row * img_height
target_x = col * img_width
res_img[target_y:target_y + img_height,
target_x:target_x + img_width] = cube_img[row * cols + col]
cv2.imwrite(target_path, res_img)
if __name__ == '__main__':
dicom_dir = './data/dicom_demo/'
slices = load_patient(dicom_dir) # 读取dicom文件的元数据(dicom tags)
pixel_spacing = slices[0].PixelSpacing # 获取dicom的spacing值
pixel_spacing.append(slices[0].SliceThickness)
print('The dicom spacing : ', pixel_spacing)
image = get_pixels_hu_by_simpleitk(dicom_dir) # 提取dicom文件中的像素值
# 标准化不同规格的图像尺寸, 统一将dicom图像缩放到1mm:1mm:1mm的尺度
image = rescale_patient_images(image, pixel_spacing, 1.00)
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)
# 加载上一步生成的png图像
pngs = load_patient_images("./temp_dir/dcm_2_png/", "*_i.png")
# 输入人工标记的结节位置: coord_x, coord_y, coord_z
cube_img = get_cube_from_img(pngs, 272, 200, 134, 64)
print(cube_img)
save_cube_img('./temp_dir/chapter3_3dcnn_img_X.png', cube_img, 8, 8)
基于深度学习的CT图像肺结节自动检测技术五—3dcnn优化模型
基于深度学习的CT图像肺结节自动检测技术三—肺有效区域提取 |
