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Yearly Archives: 2015
At the beginning of November our “A Multilevel Acceleration for l1-regularized Logistic Regression” work on how to accelerate the L1-regularized logistic regression problem was accepted to the Optimization workshop at NIPS 2015. Last week, I presented the work in the Optimization workshop at NIPS 2015. This year the Optimization workshop grew a lot, having about 50 posters in several optimization topics.
This work was a collaboration between Earn Treister (Univ. Of British Columbia) and myself (Intel Labs).
A recent paper “Clutter Mitigation in Echocardiography using Sparse Signal Separation” has been accepted for publication. The article discuss how to apply a sparsity prior to separate clutter from tissue in cardiac ultrasound images. The suggested method uses an adaptive dictionary learned from the patient data using K-SVD. The main challenge of this work was to separate the tissue and the clutter atoms as the trained dictionary includes atoms from both signals. A good separation of the dictionary yields a state-of-the-art clutter mitigation. We tested the robustness of the method and demonstrated its capabilities in real-world sequences.
In incoming weeks, the article will be published in the International Journal on Biomedical Imaging and this post will be updated once the paper is online.
(Update) The paper has been published online with open access in the International Journal on Biomedical Imaging.
Last month, I finished my PhD studies and from a few days ago I am a Doctor in Philosophy. My dissertation can be found in the Theses webpage of the Department of Computer Science website from the Technion. The dissertation describes several ways to exploit sparsity as prior information for signal modeling, for signal processing applications, and for parameter estimation.
In the works “Clutter Mitigation in Echocardiography using Sparse Signal Separation”, “Sparse Signal Separation with an Off-line Learned Dictionary for Clutter Reduction in Echocardiography“, and “Fusion of Ultrasound Harmonic Imaging with Clutter Removal Using Sparse Signal Separation“, we implemented the algorithm using an Orthogonal Matching Pursuit (OMP) and K-SVD version that works with complex valued signals. I have released the code in the software section. This code is based on the toolboxes published by Dr. Ron Rubinstein and work with Matlab. We used this code to compute sparse representations for signals with phase that were acquired from an ultrasound scanner.
You are welcome to test it and send me your feedback.
Today I received the announcement that the paper “Fusion of Ultrasound Harmonic Imaging with Clutter Removal Using Sparse Signal Separation” was accepted for a presentation in the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015).
The work introduces a novel idea on how speckle noise can be reduced by using a fusion of the fundamental and 2nd harmonics acquired simultaneously. The idea is to remove clutter artifacts while fusing the two harmonic signals. We base the solution on our previous work on clutter mitigation using MCA and the idea of joint sparsity. The method results in improved images both in clutter mitigation and speckle noise reduction.
The conference will take place during April 19th – 24th, 2015 in the wonderful city of Brisbane, Australia.
I released the code for the paper “A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation” that was presented in NIPS 2014. The algorithm includes a flag that enables the multilevel acceleration. This flag is very useful for large-scale problems on the thousands-millions of variables. The code runs in Matlab and includes some functions in C that require compilation. Also, it calls functions from METIS 5.0.2 to partitioning the neighbors in every sweep. The released version was tested on Windows, although it should work on other platforms as well.
You are welcome to try it and contact me with any comment you may have. I would like to know if somebody managed to run it in linux or mac.