Preprocessing and Augmentation

Filtering, denoising, random intensity augmentation

Image preprocessing and image augmentation prepare data for advanced medical image analysis. Use image preprocessing to reduce image acquisition artifacts and format data for your use case. For example, you can remove noise, normalize intensity values, or resize image voxels. Use image augmentation to increase the amount and variability of training data for deep learning. 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 Medical Image Preprocessing.

Functions

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Filtering and Denoising

`specklefilt`	Filter image using speckle-reducing anisotropic diffusion (Since R2022b)
`imfilter`	N-D filtering of multidimensional images
`medfilt2`	2-D median filtering
`medfilt3`	3-D median filtering
`imgaussfilt`	2-D Gaussian filtering of images
`imgaussfilt3`	3-D Gaussian filtering of 3-D images
`fspecial`	Create predefined 2-D filter
`fspecial3`	Create predefined 3-D filter

Augmentation

`jitterIntensity`	Randomly augment intensity of grayscale image or intensity volume (Since R2022b)
`randomWindow2d`	Randomly select rectangular region in image (Since R2021a)
`randomCropWindow3d`	Create randomized cuboidal cropping window
`centerCropWindow2d`	Create rectangular center cropping window
`centerCropWindow3d`	Create cuboidal center cropping window
`Rectangle`	Spatial extents of 2-D rectangular region
`Cuboid`	Spatial extents of 3-D cuboidal region
`randomAffine2d`	Create randomized 2-D affine transformation
`randomAffine3d`	Create randomized 3-D affine transformation
`affineOutputView`	Create output view for warping images
`imerase`	Remove image pixels within rectangular region of interest (Since R2021a)

Topics

Medical Image Preprocessing
Learn common preprocessing steps used in medical image analysis.
Get Started with Image Preprocessing and Augmentation for Deep Learning
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping.
Train and Apply Denoising Neural Networks
Use a pretrained neural network to remove Gaussian noise from a grayscale image, or train your own network using predefined layers.

Related Information

Image Filtering and Enhancement

Featured Examples

Unsupervised Medical Image Denoising Using CycleGAN

Generate high-quality high-dose computed tomography (CT) images from noisy low-dose CT images using a CycleGAN neural network.

Unsupervised Medical Image Denoising Using UNIT

Generate high-quality computed tomography (CT) images from noisy low-dose CT images using a UNIT neural network.