More on Compressive Sensing for enhancing imaging technology including THz

MY NOTE: IGOR CARRON, CONTINUES TO BLOG ABOUT ONE OF THE MOST INTERESTING ADVANCES IN CONTEMPORARY THOUGHT, WHICH IS COMPRESSIVE SENSING. I DON'T HAVE EITHER THE MATHEMATICAL, OR TECHNICAL BACKGROUND,  TO UNDERSTAND MORE THAN, "BROAD OUTLINES", OF THE IDEAS BEHIND MANY DEVELOPMENTS GOING ON IN THIS AREA. IT'S OBVIOUS TO ME, THAT COMPRESSIVE SENSING WILL CHANGE HOW IMAGING TECHNOLOGIES WILL BE UNDERSTOOD AND  DEVELOPED IN THE FUTURE.
THE FOLLOWING,  IS ONE SMALL PIECE, FROM IGOR'S BLOG, NUIT BLANCHE,  WHICH IS HIS REPOST, OF AN ABSTRACT, ( WHICH WILL BE OF INTEREST TO MANY READERS INTERESTED IN MEDICAL IMAGING, WITH THz OR OTHER SCANNING TECHNOLOGY.)
http://nuit-blanche.blogspot.com/2011/09/its-stunning-and-quite-amazingly-rich.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+blogspot%2FwCeDd+%28Nuit+Blanche%29





Generative-Discriminative Basis Learning for Medical Imaging by Nematollah K. Batmanghelich, Ben Taskar, Christos Davatzikos. The abstract reads:

This paper presents a novel dimensionality reduction method for classification in medical imaging. The goal is to transform very high-dimensional input (typically, millions of voxels) to a low-dimensional representation (small number of constructed features) that preserves discriminative signal and is clinically interpretable. We formulate the task as a constrained optimization problem that combines generative and discriminative objectives and show how to extend it to the semisupervised learning (SSL) setting. We propose a novel largescale algorithm to solve the resulting optimization problem. In the fully supervised case, we demonstrate accuracy rates that are better than or comparable to state-of-the-art algorithms on several datasets while producing a representation of the group difference that is consistent with prior clinical reports. Effectiveness of the proposed algorithm for SSL is evaluated with both benchmark and medical imaging datasets. In the benchmark datasets, the results are better than or comparable to the state-of-the-art methods for SSL. For evaluation of the SSL setting in medical datasets, we use images of subjects with Mild Cognitive Impairment (MCI), which is believed to be a precursor to Alzheimer’s disease (AD), as unlabeled data. AD subjects and Normal Control (NC) subjects are used as labeled data, and we try to predict conversion from MCI to AD on follow-up. The semi-supervised extension of this method not only improves the generalization accuracy for the labeled data (AD/NC) slightly but is also able to predict subjects which are likely to converge to AD