Clutter Mitigation in Echocardiography Using Sparse Signal Separation
In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. 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...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2015/958963 |
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| author | Javier S. Turek Michael Elad Irad Yavneh |
| author_facet | Javier S. Turek Michael Elad Irad Yavneh |
| author_sort | Javier S. Turek |
| collection | DOAJ |
| description | In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate
diagnosis. 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 a
sparse 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. In
experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB. |
| format | Article |
| id | doaj-art-a042f1c3e69f430282391434b3cffb36 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a042f1c3e69f430282391434b3cffb362025-02-03T01:31:33ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962015-01-01201510.1155/2015/958963958963Clutter Mitigation in Echocardiography Using Sparse Signal SeparationJavier S. Turek0Michael Elad1Irad Yavneh2Department of Computer Science, Israel Institute of Technology (Technion), 3200003 Haifa, IsraelDepartment of Computer Science, Israel Institute of Technology (Technion), 3200003 Haifa, IsraelDepartment of Computer Science, Israel Institute of Technology (Technion), 3200003 Haifa, IsraelIn ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. 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 a sparse 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. In experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB.http://dx.doi.org/10.1155/2015/958963 |
| spellingShingle | Javier S. Turek Michael Elad Irad Yavneh Clutter Mitigation in Echocardiography Using Sparse Signal Separation International Journal of Biomedical Imaging |
| title | Clutter Mitigation in Echocardiography Using Sparse Signal Separation |
| title_full | Clutter Mitigation in Echocardiography Using Sparse Signal Separation |
| title_fullStr | Clutter Mitigation in Echocardiography Using Sparse Signal Separation |
| title_full_unstemmed | Clutter Mitigation in Echocardiography Using Sparse Signal Separation |
| title_short | Clutter Mitigation in Echocardiography Using Sparse Signal Separation |
| title_sort | clutter mitigation in echocardiography using sparse signal separation |
| url | http://dx.doi.org/10.1155/2015/958963 |
| work_keys_str_mv | AT javiersturek cluttermitigationinechocardiographyusingsparsesignalseparation AT michaelelad cluttermitigationinechocardiographyusingsparsesignalseparation AT iradyavneh cluttermitigationinechocardiographyusingsparsesignalseparation |