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International Journal of Biomedical Imaging - Volume 2015 2015, Article ID 958963, 18 pages -

Research ArticleDepartment of Computer Science, Israel Institute of Technology Technion, 3200003 Haifa, Israel

Received 7 August 2014; Revised 23 March 2015; Accepted 30 March 2015

Academic Editor: Michael W. Vannier

Copyright © 2015 Javier S. Turek et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In ultrasound imaging, clutter artifacts degrade images and may cause inaccuratediagnosis. In this paper, we apply a method called Morphological Component Analysis MCA for sparse signal separation with the objective of reducing such clutter artifacts. The MCA approach assumes that the two signals in the additive mix have each asparse representation under some dictionary of atoms a matrix, and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning the dictionary from the echo data. MCA is compared to Singular Value Filtering SVF, a Principal Component Analysis- PCA- based filtering technique, and to a high-pass Finite Impulse Response FIR filter. Each filter is applied to a simulated hypoechoic lesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter and obtain results comparable to the SVF method in terms of contrast-to-noise ratio CNR. Furthermore, MCA shows a lower impact on tissue sections while removing the clutter artifacts. Inexperimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB.





Autor: Javier S. Turek, Michael Elad, and Irad Yavneh

Fuente: https://www.hindawi.com/



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