Adaptive group sparse representation in fetal echocardiogram segmentation

This paper present a novel group sparse representation model to segment four cardiac chambers in fetal echocardiograms. By incorporating the group reconstruction error, sparsity and distinctive term in a unified framework, the model is able to exploit the inherent structure of echocardiograms, constructing a discriminative group dictionary. A novel adaptive group dictionary learning approach is developed to obtain a compact dictionary with high atoms' utilization and low complexity. With the learned dictionary, the reconstruction residue is employed to discriminate the initial location of four chambers. The local appearances are used to regionally refine the final contours. Extensive experiments have been conducted to evaluate the performance of our proposed AGDL and its application in the fetal echocardiogram segmentation. The results demonstrate both representation and discriminative power of our approach and its superior performances to other competing state-of-the-art sparse representation methods and general intensity models. Our approach is capable of learning a more compact and discriminative group dictionary, providing robust and accurate four-chamber segmentation results.