SimpleITK模块以多种语言为 ITK 提供简化的接口，支持Python、R、Java、C#、Lua、Ruby、TCL 和 C++ 中的开源多维图像分析，由 Insight Toolkit 社区为生物医学科学及其他领域开发。
官方文档链接：https://simpleitk.org/#
在实际使用中，医学影像标注nii.gz文件的读取与保存用它比较方便。
nii.gz文件的读取
import SimpleITK as sitk
import skimage.io as io
def read_img(path):
img = sitk.ReadImage(path)
data = sitk.GetArrayFromImage(img)
# print(data.shape)
return data
nii.gz文件保存为标注json文件
import os
import numpy as np
import cv2
import SimpleITK as sitk
import json
def read_img(path): # 读取nii.gz文件
img = sitk.ReadImage(path)
data = sitk.GetArrayFromImage(img)
# print(data.shape)
return data
def save_json(save_path, dict_name): # 存为json文件
jsonData = json.dumps(dict_name)
with open(save_path,'w') as jsonf:
jsonf.write(jsonData)
def copy_ (back, img, size): # 标注之外的地方作为背景类
# print(type(img), img.shape)
for i in range(size[0]):
for j in range(size[1]):
if img[i][j] > 0:
back[i][j] = 0
return back
def resize_ann(img, size): # 将图像转换到需要的尺寸
path = 'mid_path.jpg' # 中间文件，先保存再以灰度图模式读取
cv2.imwrite(path, img)
# print(path,size)
original_img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# print(original_img.shape)
resize_img = cv2.resize(original_img, size, cv2.INTER_NEAREST)
# print(resize_img.shape)
# cv.imwrite('save_path.jpg', resize_img)
return resize_img
def save_ann(path1, path2):
# 例如有两种标注，分别存在path1和path2，且含有多层
img_arr1 = read_img(path1)
img_arr2 = read_img(path2)
x=0
img_arr_size = img_arr1.shape
# print(img_arr.shape)
# 读取niigz
for i in range(img_arr1.shape[0]):
img_need1 = img_arr1[i,:,:]
img_need2 = img_arr2[i,:,:]
# 检查是否需要转置
# img_need = img_need.T
# print(img_need)
# 检查标注值
# print(np.max(img_need))
# 统一尺寸大小为(512,512)
resize_size = (512,512)
if np.max(img_need1) == 0:
im_0 = np.zeros((512,512), dtype="uint8")
else:
# 如果尺寸不一需要转换
im_0 = resize_ann(img_need1, resize_size)
im_0[im_0>1]=1
if np.max(img_need2) == 0:
im_1 = np.zeros((512,512), dtype="uint8")
else:
im_1 = resize_ann(img_need2, resize_size)
im_1[im_1>1]=1
back = np.ones((512,512), dtype="uint8")
back = copy_(back, im_0, resize_size)
back = copy_(back, im_1, resize_size)
im_0 = im_0.tolist()
im_1 = im_1.tolist()
back = back.tolist()
imglist = []
for x in [im_0, im_1, back]:
imglist.append(x)
if not os.path.exists(path_ann): # 如果不存在就创建文件夹
os.mkdir(path_ann)
path_ann = 'save_path' + 'save_name'
save_json(path_ann, imglist)
分割结果保存为nii.gz文件
import os
import numpy as np
import cv2
import SimpleITK as sitk
import json
def save_niigz(save_path, save_pred):
# 如果有标注可能需要继承相关参数，包括图像尺寸等
img = sitk.ReadImage('ann_path')
# 获取标注数组
# nii_img = sitk.GetArrayFromImage(img)
# 从标注获取相关参数
origin = img.GetOrigin() #order: x, y, z
type = img.GetOrigin() #order: x, y, z
spacing = img.GetSpacing() #order:x, y, z
direction = img.GetDirection()
size = img.GetSize()
size = (size[0], size[1])
# print(size)
# 修改数据类型，例如分类信息用sitkLabel
pixelType = sitk.sitkLabelUInt8
out = sitk.Image(size,pixelType)
res = []
for i in range(len(save_res)):
# 保证每一层的尺寸统一
res.append(resize_ann(save_pred[i], size, ))
out = sitk.GetImageFromArray(res)
out.SetDirection(direction)
out.SetSpacing(spacing)
out.SetOrigin(origin)
# 直接输出可以查看参数信息
# print(out)
sitk.WriteImage(out, save_path)
其他操作参考以下笔记：
http://www.360doc.com/content/20/1128/18/460_948421999.shtml
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文章来源: 博客园
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