Learning Features from Medical Radiofrequency Ultrasonic Signals by Independent Component Analysis
Mahdi Tabassian, Nicola Testoni, Luca De Marchi, Francesca Galluzzo, N. Speciale, Guido Masetti · 2014
This paper proposes the use of independent component analysis (ICA) method for learning features from radio frequency (RF) ultrasonic signals. Conventional feature extractors usually suffer from limitations caused by some of their assumptions about the structure of imaged organ and the interaction between ultrasonic signal and tissue. ICA, on the other hand, is a data-driven approach which learns efficient representation of data by maximizing independence of some basis functions that describe the important structures of the data. It has less restrictive considerations about formation of data and as a consequence, could adapt itself to the characteristics of data with different natures. These features of ICA make it a proper candidate for dealing with the problem of feature extraction from medical ultrasound signals where variations in tissue structures and data acquisition conditions are considerable. Experimental results on raw backscattered RF signals of prostate gland show favorable performance of ICA as compared with the conventional methods designed for extracting feature from RF signals.