Image Pre-processing for Model Training

Summary

Intensity normalization

Intensity normalization is good practice and should always be done prior to using data for training. Making all of your intensity values fall within a small range that is close to zero helps the weights on our convolutional filters stay under control

There are two types of normalization that you can perform.

• zero-meaning: subtract that mean intensity value from every pixel.
• standardization: subtract the mean from each pixel and divide by the image’s standard deviation.

Image augmentation

Image augmentation allows us to create different versions of the original data. Keras provides ImageDataGenerator package for image augmentation.

Note: not all image augmentation method is appropriate for medical imaging. A vertical flip should never be applied. And validation data should NEVER be augmented.

Image resize

CNNs have an input layer that specifies the size of the image they can process. Keras flow_from_directory have a target_size parameter to resize image.