阿里云服务器网站目录,贸易公司如何做英文网站,中国网站排名榜,宝安logo设计DICOMDICOM#xff08;Digital Imaging and Communications in Medicine#xff09;即医学数字成像和通信#xff0c;是医学图像和相关信息的国际标准#xff08;ISO 12052#xff09;。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#xff0c;可用于处理、…DICOMDICOMDigital 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。 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。 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。 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。 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, orderF) 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 files 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, dtypeRefDs.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, tftf, spacingimg.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mudicomhttps://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?__bizMzAxOTk4NTIwMwmid2247483968idx1sn2844930d81f3e1f45260338dae21d8eachksm9c3fe41cab486d0aa9d03a6494865c0ffdbb0e878f4804227a73d2d403382432fe22e9c03b71mpshare1scene23srcid0122xHKRmyLclSvbojaS1ICX##
http://blog.csdn.net/langb2014/article/details/54667905#
http://www.jianshu.com/p/df64088e9b6b
