Classification of cardiac magnetic resonance image type and orientation

Cardiac magnetic resonance imaging provides a number of different imaging acquisition types and views of different body cross sections and orientations. A huge amount of images are produced which demand an automatic method for classification based on the visual contents to facilitate diagnosis and searching operations. In this work, we propose a fully automated classification method for classifying cardiac MRI images according to image acquisition type and orientation. Local binary pattern is used to represent the texture differences among the different image types. Edge orientation histogram is used to differentiate the different image orientations. In addition, two similarity measures are applied and compared: log-likelihood and chi-square distance. The chi-square similarity measure showed better results than the log-likelihood. An average accuracy for classifying the image type and orientation using chi-square was respectively 97% and 96%.