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Clutter Mitigation in Echocardiography Using Sparse Signal Separation.

Turek JS, Elad M, Yavneh I - Int J Biomed Imaging (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Israel Institute of Technology (Technion), 3200003 Haifa, Israel.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Block diagram of Morphological Component Analysis for clutter reduction for a signal patch si.
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fig2: Block diagram of Morphological Component Analysis for clutter reduction for a signal patch si.

Mentions: Clutter reduction in a patch si is achieved by removing the clutter component Dcxci from it; that is,(10)s^i=si−Dcxci,where is the resulting patch with reduced clutter. This requires computing the sparse representations xti and xci in (9). Note that (9) can be rewritten as follows:(11)si=Dt ∣ Dcxtixci+ni=Dxi+ni,where D is the concatenated dictionary with the tissue and clutter subdictionaries and xi is the concatenation of the sparse representations of the tissue and clutter signals of the patch. Consequently, solving (4) or (5) with the concatenated dictionary D yields the concatenated sparse representation xi. The representations xti and xci are obtained from xi relatively to the tissue or clutter atom positions in the concatenated dictionary. In this work, the Orthogonal Matching Pursuit (OMP) [19] algorithm is used to find an approximation to the sparse vector xi. The complete clutter reduction procedure for a patch si is illustrated in Figure 2. Additionally, for solving the problem in (4) or (5), the dictionary D must be known. An adaptive dictionary D allows the method to learn the patient's own physiological characteristics and improve the results. Hence, such a dictionary D is learned adaptively from the signal patches {si}i=1P using the K-SVD algorithm [41].


Clutter Mitigation in Echocardiography Using Sparse Signal Separation.

Turek JS, Elad M, Yavneh I - Int J Biomed Imaging (2015)

Block diagram of Morphological Component Analysis for clutter reduction for a signal patch si.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4495184&req=5

fig2: Block diagram of Morphological Component Analysis for clutter reduction for a signal patch si.
Mentions: Clutter reduction in a patch si is achieved by removing the clutter component Dcxci from it; that is,(10)s^i=si−Dcxci,where is the resulting patch with reduced clutter. This requires computing the sparse representations xti and xci in (9). Note that (9) can be rewritten as follows:(11)si=Dt ∣ Dcxtixci+ni=Dxi+ni,where D is the concatenated dictionary with the tissue and clutter subdictionaries and xi is the concatenation of the sparse representations of the tissue and clutter signals of the patch. Consequently, solving (4) or (5) with the concatenated dictionary D yields the concatenated sparse representation xi. The representations xti and xci are obtained from xi relatively to the tissue or clutter atom positions in the concatenated dictionary. In this work, the Orthogonal Matching Pursuit (OMP) [19] algorithm is used to find an approximation to the sparse vector xi. The complete clutter reduction procedure for a patch si is illustrated in Figure 2. Additionally, for solving the problem in (4) or (5), the dictionary D must be known. An adaptive dictionary D allows the method to learn the patient's own physiological characteristics and improve the results. Hence, such a dictionary D is learned adaptively from the signal patches {si}i=1P using the K-SVD algorithm [41].

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Israel Institute of Technology (Technion), 3200003 Haifa, Israel.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus