python中的三维多模态医学图像配准
我在Python中搜索并找到了很多2D图像注册图像,但这些图像不能满足我的需要。我有几张MRI和CT图像,都是在同一个病人身上拍摄的,我想知道是否有人有Python代码示例,可以为这些医学图像执行3D刚性图像注册。尝试使用MeVisLab轻松完成这项工作。此外,您可以尝试FSL以获得更可靠的结果 签出SimpleTk或Itk库。它们提供了现成的图像配准算法。对于3D注册,您只需传递卷以进行注册,如.mhd或.mha文件,然后使用3D变换 您也可以参考此链接您可以尝试lcreg,请参阅。pypi页面提供了指向教程和示例数据的链接python中的三维多模态医学图像配准,python,image-processing,3d,medical,Python,Image Processing,3d,Medical,我在Python中搜索并找到了很多2D图像注册图像,但这些图像不能满足我的需要。我有几张MRI和CT图像,都是在同一个病人身上拍摄的,我想知道是否有人有Python代码示例,可以为这些医学图像执行3D刚性图像注册。尝试使用MeVisLab轻松完成这项工作。此外,您可以尝试FSL以获得更可靠的结果 签出SimpleTk或Itk库。它们提供了现成的图像配准算法。对于3D注册,您只需传递卷以进行注册,如.mhd或.mha文件,然后使用3D变换 您也可以参考此链接您可以尝试lcreg,请参阅。pypi页
you can use python in SimpleITK:
https://itk.org/Wiki/SimpleITK/Tutorials/MICCAI2015
import SimpleITK as sitk
def save_combined_central_slice(fixed, moving, transform, file_name_prefix):
global iteration_number
alpha = 0.7
central_indexes = [i/2 for i in fixed.GetSize()]
moving_transformed = sitk.Resample(moving, fixed, transform,
sitk.sitkLinear, 0.0,
moving_image.GetPixelIDValue())
#extract the central slice in xy, xz, yz and alpha blend them
combined = [(1.0 - alpha)*fixed[:,:,central_indexes[2]] + \
alpha*moving_transformed[:,:,central_indexes[2]],
(1.0 - alpha)*fixed[:,central_indexes[1],:] + \
alpha*moving_transformed[:,central_indexes[1],:],
(1.0 - alpha)*fixed[central_indexes[0],:,:] + \
alpha*moving_transformed[central_indexes[0],:,:]]
#resample the alpha blended images to be isotropic and rescale intensity
#values so that they are in [0,255], this satisfies the requirements
#of the jpg format
combined_isotropic = []
for img in combined:
original_spacing = img.GetSpacing()
original_size = img.GetSize()
min_spacing = min(original_spacing)
new_spacing = [min_spacing, min_spacing]
new_size = [int(round(original_size[0]*(original_spacing[0]/min_spacing))),
int(round(original_size[1]*(original_spacing[1]/min_spacing)))]
resampled_img = sitk.Resample(img, new_size, sitk.Transform(),
sitk.sitkLinear, img.GetOrigin(),
new_spacing, img.GetDirection(), 0.0,
img.GetPixelIDValue())
combined_isotropic.append(sitk.Cast(sitk.RescaleIntensity(resampled_img),
sitk.sitkUInt8))
#tile the three images into one large image and save using the given file
#name prefix and the iteration number
sitk.WriteImage(sitk.Tile(combined_isotropic, (1,3)),
file_name_prefix+ format(iteration_number, '03d') + '.jpg')
iteration_number+=1
#read the images
fixed_image = sitk.ReadImage("training_001_ct.mha", sitk.sitkFloat32)
moving_image = sitk.ReadImage("training_001_mr_T1.mha", sitk.sitkFloat32)
#initial alignment of the two volumes
transform = sitk.CenteredTransformInitializer(fixed_image,
moving_image,
sitk.Euler3DTransform(),
sitk.CenteredTransformInitializerFilter.GEOMETRY)
#multi-resolution rigid registration using Mutual Information
registration_method = sitk.ImageRegistrationMethod()
registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)
registration_method.SetMetricSamplingPercentage(0.01)
registration_method.SetInterpolator(sitk.sitkLinear)
registration_method.SetOptimizerAsGradientDescent(learningRate=1.0,
numberOfIterations=100,
convergenceMinimumValue=1e-6,
convergenceWindowSize=10)
registration_method.SetOptimizerScalesFromPhysicalShift()
registration_method.SetShrinkFactorsPerLevel(shrinkFactors = [4,2,1])
registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[2,1,0])
registration_method.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()
registration_method.SetInitialTransform(transform)
#add iteration callback, save central slice in xy, xz, yz planes
global iteration_number
iteration_number = 0
registration_method.AddCommand(sitk.sitkIterationEvent,
lambda: save_combined_central_slice(fixed_image,
moving_image,
transform,
'output/iteration'))
registration_method.Execute(fixed_image, moving_image)
sitk.WriteTransform(transform, 'output/ct2mrT1.tfm')