这篇教程基于深度学习的CT图像肺结节自动检测技术三—肺有效区域提取写得很实用，希望能帮到您。
# 肺有效区域提取
import SimpleITK
from scipy import ndimage as ndi
from skimage.segmentation import clear_border
from skimage.measure import label, regionprops
from skimage.morphology import disk, dilation, binary_erosion, binary_closing
from skimage.filters import roberts, sobel
import cv2
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)
:param src_dir: dicom文件夹路径
:return: image array
'''
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
: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 get_segmented_lungs(im):
'''
对输入的图像进行肺部区域分割，提取有效的肺部区域，用于模型训练
:param 输入的图像
:return: 返回分割结果
'''
binary = im < -400 # Step 1: 转换为二值化图像
cleared = clear_border(binary) # Step 2: 清除图像边界的小块区域
label_image = label(cleared) # Step 3: 分割图像
areas = [r.area for r in regionprops(label_image)] # Step 4: 保留2个最大的连通区域
areas.sort()
if len(areas) > 2:
for region in regionprops(label_image):
if region.area < areas[-2]:
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
selem = disk(2) # Step 5: 图像腐蚀操作,将结节与血管剥离
binary = binary_erosion(binary, selem)
selem = disk(10) # Step 6: 图像闭环操作,保留贴近肺壁的结节
binary = binary_closing(binary, selem)
edges = roberts(binary) # Step 7: 进一步将肺区残余小孔区域填充
binary = ndi.binary_fill_holes(edges)
get_high_vals = binary == 0 # Step 8: 将二值化图像叠加到输入图像上
im[get_high_vals] = -2000
print('lung segmentation complete.')
return im, binary
if __name__ == '__main__':
dicom_dir = './data/dicom_demo/'
# 提取dicom文件中的像素值
image = get_pixels_hu_by_simpleitk(dicom_dir) #图片像素提取
im, binary = get_segmented_lungs(image[133]) #肺部区域分割
org_img = normalize_hu(image[133]) #【0,1】归一化
cv2.imwrite('./temp_dir/chapter3_segmentation_im.png', org_img*255)
cv2.imwrite('./temp_dir/chapter3_segmentation_binary.png', binary*255)
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
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） |
