Automatic apical view classification of echocardiograms using a discriminative learning dictionary

    loading  Checking for direct PDF access through Ovid


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.

Related Topics

    loading  Loading Related Articles