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preprocessing and augmentation -凯发k8网页登录

3-d registration and denoising, random intensity augmentation

image preprocessing and image augmentation prepare data for advanced medical image analysis workflows. use image preprocessing to reduce image acquisition artifacts and format data for the target workflow. for example, you can remove noise, normalize intensity values, resize image voxels, or align images using registration. use image augmentation to increase the amount and variability of training data for deep learning workflows. for example, you can randomly adjust image contrast or apply random rotations or scaling to simulate variations in image acquisition and patient anatomy. to get started, see .

functions

filter image using speckle-reducing anisotropic diffusion
n-d filtering of multidimensional images
2-d median filtering
3-d median filtering
2-d gaussian filtering of images
3-d gaussian filtering of 3-d images
create predefined 2-d filter
create predefined 3-d filter

fast registration

fast registration of grayscale images or intensity volumes using moment of mass method

optimization-based registration

intensity-based image registration
estimate geometric transformation that aligns two 2-d or 3-d images
configurations for intensity-based registration
mattes mutual information metric configuration
mean square error metric configuration
regular step gradient descent optimizer configuration
one-plus-one evolutionary optimizer configuration

deformable registration

deformable registration of grayscale images or intensity volumes using total variation method
estimate displacement field that aligns two 2-d or 3-d images

groupwise registration

groupwise deformable registration

correlation-based registration

estimate geometric transformation that aligns two 2-d images using phase correlation
normalized 2-d cross-correlation

surface registration

surface registration using iterative closest point algorithm

apply transformation

apply geometric transformation to image
resample medical image volume in different patient coordinate system
randomly augment intensity of grayscale image or intensity volume
randomly select rectangular region in image
create randomized cuboidal cropping window
create rectangular center cropping window
create cuboidal center cropping window
spatial extents of 2-d rectangular region
spatial extents of 3-d cuboidal region
create randomized 2-d affine transformation
create randomized 3-d affine transformation
create output view for warping images
remove image pixels within rectangular region of interest

topics


  • learn common preprocessing steps used in medical image analysis workflows.

  • medical image registration

    techniques for alignment of medical images, volumes, and surfaces.


  • preprocess data with deterministic operations such as normalization or color space conversion, or augment training data with randomized operations such as random cropping or color jitter.


  • use a pretrained neural network to remove gaussian noise from a grayscale image, or train your own network using predefined layers.

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