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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.
Today I received the announcement that the paper “Fusion of Ultrasound Harmonic Imaging with Clutter Removal Using Sparse Signal Separation” was accepted for a presentation in the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015).
The work introduces a novel idea on how speckle noise can be reduced by using a fusion of the fundamental and 2nd harmonics acquired simultaneously. The idea is to remove clutter artifacts while fusing the two harmonic signals. We base the solution on our previous work on clutter mitigation using MCA and the idea of joint sparsity. The method results in improved images both in clutter mitigation and speckle noise reduction.
The conference will take place during April 19th – 24th, 2015 in the wonderful city of Brisbane, Australia.
Our work on Sparse Signal Separation for Clutter Reduction in Echocardiography using Off-line Learned Dictionaries was accepted to be presented in IEEE 28th Convention of Electrical and Electronics Engineers in Israel. The conference will be held in Eilat during December, 2014. The work is about removing clutter artifacts from ultrasound images using sparse representations, morphological component analysis, and off-line dictionary learning.