Unsupervised image classification of medical ultrasound data by multiresolution elastic registration

Thousands of medical images are saved in databases every day and the need for algorithms able to handle such data in an unsupervised manner is steadily increasing. The classification of ultrasound images is an outstandingly difficult task, due to the high noise level of these images. We present a detailed description of an algorithm based on multiscale elastic registration capable of unsupervised, landmark-free classification of cardiac ultrasound images into their respective views (apical four chamber, two chamber, parasternal long axis and short axis views). We validated the algorithm with 90 unselected, consecutive echocardiographic images recorded during daily clinical work. When the two visually very similar apical views (four chamber and two chamber) are combined into one class, we obtained a 93.0% correct classification (chi2 = 123.8, p < 0.0001, cross-validation 93.0%; chi2 = 131.1, p < 0.0001). Classification into the 4 classes reached a 90.0% correct classification (chi2 = 205.4, p < 0.0001, cross-validation 82.2%; chi2 = 165.9, p < 0.0001).