Discrete Fourier Analysis of Ultrasound RF Time Series for Detection of Prostate Cancer

In this paper, we demonstrate that a set of six features extracted from the discrete Fourier transform of ultrasound radio-frequency (RF) time series can be used to detect prostate cancer with high sensitivity and specificity. Ultrasound RF time series refer to a series of echoes received from one spatial location of tissue while the imaging probe and the tissue are fixed in position. Our previous investigations have shown that at least one feature, fractal dimension, of these signals demonstrates strong correlation with the tissue microstructure. In the current paper, six new features that represent the frequency spectrum of the RF time series have been used, in conjunction with a neural network classification approach, to detect prostate cancer in regions of tissue as small as 0.03 cm². Based on pathology results used as gold standard, we have acquired mean accuracy of 91%, mean sensitivity of 92% and mean specificity of 90% on seven human prostates.