As part of striving towards fully automatic cardiac functional assessment of echocardiograms, automatic classification of their standard views is essential as a pre-processing stage. The similarity among three of the routinely acquired longitudinal scans: apical two-chamber (A2C), apical four-chamber (A4C) and apical long-axis (ALX), and the noise commonly inherent to these scans - make the classification a challenge. Here we introduce a multi-stage classification algorithm that employs spatio-temporal feature extraction (Cuboid Detector) and supervised dictionary learning (LC-KSVD) approaches to uniquely enhance the automatic recognition and classification accuracy of echocardiograms. The algorithm incorporates both discrimination and labelling information to allow a discriminative and sparse representation of each view. The advantage of the spatio-temporal feature extraction as compared to spatial processing is then validated.
A set of 309 clinical clips (103 for each view), were labeled by 2 experts. A subset of 70 clips of each class was used as a training set and the rest as a test set. The recognition accuracies achieved were: 97%, 91% and 97% of A2C, A4C and ALX respectively, with average recognition rate of 95%. Thus, automatic classification of echocardiogram views seems promising, despite the inter-view similarity between the classes and intra-view variability among clips belonging to the same class.