Abstract: The present invention relates to automatic calibration of gamma value. In particular, it relates to automatically setting gamma value for the pre-processing of medical images, whereby the input image is standardised for the purpose of training machine learning models. The method is useful for useful for processing purposes to ensure that features may be identified reliably and consistently across different images by the same image processing methods.

Abstract: The present invention relates to training data sets and a system and method for generating training images especially those which are medical images. Especially disclosed is a method of training a machine learning model to recognize movement of a body part in an acquired medical image The machine learning model is trained by varying/modifying a blur convolution kernel constructed with pixels oriented in a direction of the movement; the method including determining at least one motion weighting factor corresponding to a motion time period when the body part is moving during acquisition of the medical image, and using the motion weighting factor to vary/modify the blur convolution kernel.