A recent paper “Clutter Mitigation in Echocardiography using Sparse Signal Separation” has been accepted for publication. The article discuss how to apply a sparsity prior to separate clutter from tissue in cardiac ultrasound images. The suggested method uses an adaptive dictionary learned from the patient data using K-SVD. The main challenge of this work was to separate the tissue and the clutter atoms as the trained dictionary includes atoms from both signals. A good separation of the dictionary yields a state-of-the-art clutter mitigation. We tested the robustness of the method and demonstrated its capabilities in real-world sequences.
In incoming weeks, the article will be published in the International Journal on Biomedical Imaging and this post will be updated once the paper is online.
(Update) The paper has been published online with open access in the International Journal on Biomedical Imaging.
Last month, I finished my PhD studies and from a few days ago I am a Doctor in Philosophy. My dissertation can be found in the Theses webpage of the Department of Computer Science website from the Technion. The dissertation describes several ways to exploit sparsity as prior information for signal modeling, for signal processing applications, and for parameter estimation.