אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

In cardiac ultrasound, clutter is an artifact that obscures parts of the heart and may cause inaccurate diagnosis. In particular, a cluttered ultrasound signal is seen as a superposition of tissue, clutter and noise components. In this work, 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 signals corresponding to the clutter and the tissue have each a sparse representation under some dictionary of atoms (a matrix), and the separation is achieved by finding these sparse representations. We present several novel alternatives to train the dictionaries adaptively both from on-line and off-line data. The presented methods outperform the state-of-the-art Singular Value Filter (SVF), show a lower impact on tissue sections, are robust to the input data characteristics, and yield state-of-the-art performance. In a joint work with J. Sulam, we further extend our approach by presenting a method based on a joint sparsity model that fuses the first and second harmonic images while performing clutter mitigation and noise reduction.