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