Python 处理医学影像学中的DICOM

DICOMDICOM（Digital Imaging and Communications in Medicine）即医学数字成像和通信，是医学图像和相关信息的国际标准（ISO 12052）。它定义了质量能满足临床需要的可用于数据交换的医学图像格式，可用于处理、存储、打印和传输医学影像信息。DICOM可以便捷地交换于两个满足DICOM格式协议的工作站之间。目前该协议标准不仅广泛应用于大型医院，而且已成为小型诊所和牙科诊所医生办公室的标准影像阅读格式。

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DICOM被广泛应用于放射医疗、心血管成像以及放射诊疗诊断设备（X射线，CT，核磁共振，超声等），并且在眼科和牙科等其它医学领域得到越来越深入广泛的应用。在数以万计的在用医学成像设备中，DICOM是部署最为广泛的医疗信息标准之一。当前大约有百亿级符合DICOM标准的医学图像用于临床使用。

目前，越来越多的DICOM应用程序和分析软件被运用于临床医学，促使越来越多的编程语言开发出支持DICOM API的框架。今天就让我来介绍一下Python语言下支持的DICOM模块，以及如何完成基本DICOM信息分析和处理的编程方法。

Pydicom

Pydicom是一个处理DICOM文件的纯Python软件包。它可以通过非常容易的“Pythonic”的方式来提取和修改DICOM数据，修改后的数据还会借此生成新的DICOM文件。作为一个纯Python包，Pydicom可以在Python解释器下任何平台运行，除了必须预先安装Numpy模块外，几乎无需其它任何配置要求。其局限性之一是无法处理压缩像素图像（如JPEG），其次是无法处理分帧动画图像（如造影电影）。

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SimpleITK

Insight Segmentation and Registration Toolkit (ITK)是一个开源、跨平台的框架，可以提供给开发者增强功能的图像分析和处理套件。其中最为著名的就是SimpleITK，是一个简化版的、构建于ITK最顶层的模块。SimpleITK旨在易化图像处理流程和方法。

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PIL

Python Image Library (PIL) 是在Python环境下不可缺少的图像处理模块，支持多种格式，并提供强大的图形与图像处理功能，而且API却非常简单易用。

OpenCV

OpenCV的全称是：Open Source Computer Vision Library。OpenCV是一个基于BSD许可（开源）发行的跨平台计算机视觉库，可以运行在Linux、Windows和Mac OS操作系统上。它轻量级而且高效——由一系列 C 函数和少量 C++ 类构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了图像处理和计算机视觉方面的很多通用算法。

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下面就让我以实际Python代码来演示如何编程处理心血管冠脉造影DICOM图像信息。

1. 导入主要框架：SimpleITK、pydicom、PIL、cv2和numpy

import SimpleITK as sitk
from PIL import Image
import pydicom
import numpy as np
import cv2

2. 应用SimpleITK框架来读取DICOM文件的矩阵信息。如果DICOM图像是三维螺旋CT图像，则帧参数则代表CT扫描层数；而如果是造影动态电影图像，则帧参数就是15帧/秒的电影图像帧数。

def loadFile(filename):ds = sitk.ReadImage(filename)img_array = sitk.GetArrayFromImage(ds)frame_num, width, height = img_array.shapereturn img_array, frame_num, width, height

3. 应用pydicom来提取患者信息。

def loadFileInformation(filename):information = {}ds = pydicom.read_file(filename)    information['PatientID'] = ds.PatientIDinformation['PatientName'] = ds.PatientNameinformation['PatientBirthDate'] = ds.PatientBirthDateinformation['PatientSex'] = ds.PatientSexinformation['StudyID'] = ds.StudyIDinformation['StudyDate'] = ds.StudyDateinformation['StudyTime'] = ds.StudyTimeinformation['InstitutionName'] = ds.InstitutionNameinformation['Manufacturer'] = ds.Manufacturerinformation['NumberOfFrames'] = ds.NumberOfFrames    return information

4. 应用PIL来检查图像是否被提取。

def showImage(img_array, frame_num = 0):img_bitmap = Image.fromarray(img_array[frame_num])return img_bitmap

5. 采用CLAHE (Contrast Limited Adaptive Histogram Equalization)技术来优化图像。

def limitedEqualize(img_array, limit = 4.0):img_array_list = []for img in img_array:clahe = cv2.createCLAHE(clipLimit = limit, tileGridSize = (8,8))img_array_list.append(clahe.apply(img))img_array_limited_equalized = np.array(img_array_list)return img_array_limited_equalized

这一步对于整个图像处理起到很重要的作用，可以根据不同的原始DICOM图像的窗位和窗宽来进行动态调整，以达到最佳的识别效果。

最后应用OpenCV的Python框架cv2把每帧图像组合在一起，生成通用视频格式。

def writeVideo(img_array):frame_num, width, height = img_array.shapefilename_output = filename.split('.')[0] + '.avi'    video = cv2.VideoWriter(filename_output, -1, 16, (width, height))    for img in img_array:video.write(img)video.release()

VTK加载DICOM数据

import vtk  
from vtk.util import numpy_support  
import numpy  PathDicom = "./dir_with_dicom_files/"  
reader = vtk.vtkDICOMImageReader()  
reader.SetDirectoryName(PathDicom)  
reader.Update()  # Load dimensions using `GetDataExtent`  
_extent = reader.GetDataExtent()  
ConstPixelDims = [_extent[1]-_extent[0]+1, _extent[3]-_extent[2]+1, _extent[5]-_extent[4]+1]  # Load spacing values  
ConstPixelSpacing = reader.GetPixelSpacing()  # Get the 'vtkImageData' object from the reader  
imageData = reader.GetOutput()  
# Get the 'vtkPointData' object from the 'vtkImageData' object  
pointData = imageData.GetPointData()  
# Ensure that only one array exists within the 'vtkPointData' object  
assert (pointData.GetNumberOfArrays()==1)  
# Get the `vtkArray` (or whatever derived type) which is needed for the `numpy_support.vtk_to_numpy` function  
arrayData = pointData.GetArray(0)  # Convert the `vtkArray` to a NumPy array  
ArrayDicom = numpy_support.vtk_to_numpy(arrayData)  
# Reshape the NumPy array to 3D using 'ConstPixelDims' as a 'shape'  
ArrayDicom = ArrayDicom.reshape(ConstPixelDims, order='F')

PYDICOM加载DICOM数据：
可以在https://github.com/darcymason/pydicom的test里面看怎么用代码。

import dicom  
import os  
import numpy  PathDicom = "./dir_with_dicom_series/"  
lstFilesDCM = []  # create an empty list  
for dirName, subdirList, fileList in os.walk(PathDicom):  for filename in fileList:  if ".dcm" in filename.lower():  # check whether the file's DICOM  lstFilesDCM.append(os.path.join(dirName,filename))  # Get ref file  
RefDs = dicom.read_file(lstFilesDCM[0])  # Load dimensions based on the number of rows, columns, and slices (along the Z axis)  
ConstPixelDims = (int(RefDs.Rows), int(RefDs.Columns), len(lstFilesDCM))  # Load spacing values (in mm)  
ConstPixelSpacing = (float(RefDs.PixelSpacing[0]), float(RefDs.PixelSpacing[1]), float(RefDs.SliceThickness))  # The array is sized based on 'ConstPixelDims'  
ArrayDicom = numpy.zeros(ConstPixelDims, dtype=RefDs.pixel_array.dtype)  # loop through all the DICOM files  
for filenameDCM in lstFilesDCM:  # read the file  ds = dicom.read_file(filenameDCM)  # store the raw image data  ArrayDicom[:, :, lstFilesDCM.index(filenameDCM)] = ds.pixel_array

转换VTK built-in types to SimpleITK/ITK built-ins and vice-versa

import vtk  
import SimpleITK  dctITKtoVTK = {SimpleITK.sitkInt8: vtk.VTK_TYPE_INT8,  SimpleITK.sitkInt16: vtk.VTK_TYPE_INT16,  SimpleITK.sitkInt32: vtk.VTK_TYPE_INT32,  SimpleITK.sitkInt64: vtk.VTK_TYPE_INT64,  SimpleITK.sitkUInt8: vtk.VTK_TYPE_UINT8,  SimpleITK.sitkUInt16: vtk.VTK_TYPE_UINT16,  SimpleITK.sitkUInt32: vtk.VTK_TYPE_UINT32,  SimpleITK.sitkUInt64: vtk.VTK_TYPE_UINT64,  SimpleITK.sitkFloat32: vtk.VTK_TYPE_FLOAT32,  SimpleITK.sitkFloat64: vtk.VTK_TYPE_FLOAT64}  
dctVTKtoITK = dict(zip(dctITKtoVTK.values(),   dctITKtoVTK.keys()))  def convertTypeITKtoVTK(typeITK):  if typeITK in dctITKtoVTK:  return dctITKtoVTK[typeITK]  else:  raise ValueError("Type not supported")  def convertTypeVTKtoITK(typeVTK):  if typeVTK in dctVTKtoITK:  return dctVTKtoITK[typeVTK]  else:  raise ValueError("Type not supported")

VTK-SimpleITK绘制数据

#!/usr/bin/python  import SimpleITK as sitk  
import vtk  
import numpy as np  from vtk.util.vtkConstants import *  def numpy2VTK(img,spacing=[1.0,1.0,1.0]):  # evolved from code from Stou S.,  # on http://www.siafoo.net/snippet/314  importer = vtk.vtkImageImport()  img_data = img.astype('uint8')  img_string = img_data.tostring() # type short  dim = img.shape  importer.CopyImportVoidPointer(img_string, len(img_string))  importer.SetDataScalarType(VTK_UNSIGNED_CHAR)  importer.SetNumberOfScalarComponents(1)  extent = importer.GetDataExtent()  importer.SetDataExtent(extent[0], extent[0] + dim[2] - 1,  extent[2], extent[2] + dim[1] - 1,  extent[4], extent[4] + dim[0] - 1)  importer.SetWholeExtent(extent[0], extent[0] + dim[2] - 1,  extent[2], extent[2] + dim[1] - 1,  extent[4], extent[4] + dim[0] - 1)  importer.SetDataSpacing( spacing[0], spacing[1], spacing[2])  importer.SetDataOrigin( 0,0,0 )  return importer  def volumeRender(img, tf=[],spacing=[1.0,1.0,1.0]):  importer = numpy2VTK(img,spacing)  # Transfer Functions  opacity_tf = vtk.vtkPiecewiseFunction()  color_tf = vtk.vtkColorTransferFunction()  if len(tf) == 0:  tf.append([img.min(),0,0,0,0])  tf.append([img.max(),1,1,1,1])  for p in tf:  color_tf.AddRGBPoint(p[0], p[1], p[2], p[3])  opacity_tf.AddPoint(p[0], p[4])  # working on the GPU  # volMapper = vtk.vtkGPUVolumeRayCastMapper()  # volMapper.SetInputConnection(importer.GetOutputPort())  # # The property describes how the data will look  # volProperty =  vtk.vtkVolumeProperty()  # volProperty.SetColor(color_tf)  # volProperty.SetScalarOpacity(opacity_tf)  # volProperty.ShadeOn()  # volProperty.SetInterpolationTypeToLinear()  # working on the CPU  volMapper = vtk.vtkVolumeRayCastMapper()  compositeFunction = vtk.vtkVolumeRayCastCompositeFunction()  compositeFunction.SetCompositeMethodToInterpolateFirst()  volMapper.SetVolumeRayCastFunction(compositeFunction)  volMapper.SetInputConnection(importer.GetOutputPort())  # The property describes how the data will look  volProperty =  vtk.vtkVolumeProperty()  volProperty.SetColor(color_tf)  volProperty.SetScalarOpacity(opacity_tf)  volProperty.ShadeOn()  volProperty.SetInterpolationTypeToLinear()  # Do the lines below speed things up?  # pix_diag = 5.0  # volMapper.SetSampleDistance(pix_diag / 5.0)      # volProperty.SetScalarOpacityUnitDistance(pix_diag)   vol = vtk.vtkVolume()  vol.SetMapper(volMapper)  vol.SetProperty(volProperty)  return [vol]  def vtk_basic( actors ):  """ Create a window, renderer, interactor, add the actors and start the thing Parameters ---------- actors :  list of vtkActors Returns ------- nothing """       # create a rendering window and renderer  ren = vtk.vtkRenderer()  renWin = vtk.vtkRenderWindow()  renWin.AddRenderer(ren)  renWin.SetSize(600,600)  # ren.SetBackground( 1, 1, 1)  # create a renderwindowinteractor  iren = vtk.vtkRenderWindowInteractor()  iren.SetRenderWindow(renWin)  for a in actors:  # assign actor to the renderer  ren.AddActor(a )  # render  renWin.Render()  # enable user interface interactor  iren.Initialize()  iren.Start()  #####  filename = 'toto.nii.gz'  img = sitk.ReadImage( filename ) # SimpleITK object  
data = sitk.GetArrayFromImage( img ) # numpy array  from scipy.stats.mstats import mquantiles  
q = mquantiles(data.flatten(),[0.7,0.98])  
q[0]=max(q[0],1)  
q[1] = max(q[1],1)  
tf=[[0,0,0,0,0],[q[0],0,0,0,0],[q[1],1,1,1,0.5],[data.max(),1,1,1,1]]  actor_list = volumeRender(data, tf=tf, spacing=img.GetSpacing())  vtk_basic(actor_list)

下面一个不错的软件：
https://github.com/bastula/dicompyler
还有一个python的库mudicom，https://github.com/neurosnap/mudicom

import mudicom  
mu = mudicom.load("mudicom/tests/dicoms/ex1.dcm")  # returns array of data elements as dicts  
mu.read()  # returns dict of errors and warnings for DICOM  
mu.validate()  # basic anonymization  
mu.anonymize()  
# save anonymization  
mu.save_as("dicom.dcm")  # creates image object  
img = mu.image # before v0.1.0 this was mu.image()  
# returns numpy array  
img.numpy # before v0.1.0 this was mu.numpy()  # using Pillow, saves DICOM image  
img.save_as_pil("ex1.jpg")  
# using matplotlib, saves DICOM image  
img.save_as_plt("ex1_2.jpg")

本文转自：

http://mp.weixin.qq.com/s?__biz=MzAxOTk4NTIwMw==&mid=2247483968&idx=1&sn=2844930d81f3e1f45260338dae21d8ea&chksm=9c3fe41cab486d0aa9d03a6494865c0ffdbb0e878f4804227a73d2d403382432fe22e9c03b71&mpshare=1&scene=23&srcid=0122xHKRmyLclSvbojaS1ICX##

http://blog.csdn.net/langb2014/article/details/54667905#

http://www.jianshu.com/p/df64088e9b6b