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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2753406
|2 doi
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|a eng
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| 100 |
1 |
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|a Ouzir, Nora
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning
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| 264 |
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|c 2018
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|a Text
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|a Date Completed 30.07.2018
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|a Date Revised 30.07.2018
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic simulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic simulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Dictionaries
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| 650 |
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4 |
|a Machine learning
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| 650 |
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4 |
|a Matching pursuit algorithms
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| 650 |
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4 |
|a Motion estimation
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| 650 |
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4 |
|a Motion measurement
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| 650 |
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4 |
|a Two dimensional displays
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4 |
|a Ultrasonic imaging
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|a Basarab, Adrian
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|a Liebgott, Herve
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1 |
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|a Harbaoui, Brahim
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| 700 |
1 |
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|a Tourneret, Jean-Yves
|e verfasserin
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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|g 27(2018), 1 vom: 18. Jan., Seite 64-77
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