A Powerful Signal Extraction Method to Improve the Clarity of Medical and Other Digital Images

University of California System: University of California, San Diego Technology Transfer Office
posted on 02/02/2012

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Independent component analysis (ICA) is a mathematical method for temporal and spatial signal extraction, where a data set of digitally recorded mixed signals is transformed into a new set of unmixed source signals. It plays an important role in signal and image processing analysis. A variety of prior methods have been used to estimate the source signals: some methods require a priori model probability density functions (pdfs) to approximate the source signal pdfs, other methods involve the optimization of a cost function, such as the mutual information (MI) between the estimated source signals. A method that does not require model pdfs and can perform a global search of the optimum solution over the entire range of angular parameters can greatly simplify the application and the robustness of ICA on a wide variety of signal and image extraction applications.

The method has been demonstrated to separate physiologic signals on dynamic medical-image data to obtain images with improved demarcations. Furthermore, the principles of the invention can be applied, in general, to extract/separate signal sources from a variety of digital signal mixtures found in typical applications of spacial and temporal independent component analysis (ICA). The potential applications include, but are not limited to, extraction of source signal of multispectral image, sensor array signal/data, and digitized audio signals.

It does not require a model or prior information on the underlying source signal, which in many cases may be unknown.

• It performs minimization simultaneously in multiple angular parameters.
• For computational speed, the algorithm has been implemented to automatically setup look-up tables and array indices tables for the Givens rotations, based on the number of angular parameters of the optimization; and it is only limited by the computational hardware memory size.
• It reveals regions of local minima, which need to be avoided in finding the optimum solution for signal separation, in polar plots of the mutual information.
• The method allows visual check for proper convergence on the global minimum (best solution) by displaying a plot of the cost function graphically over the full range of angular parameters (0-180°).