Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series

Abstract

The aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features.Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchi's approach. A support vector machine (SVM) classifier was used to classify the ROIs.The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features.

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

“…Spectral features have been recognized as the most impactful domain for analyzing RF time series [ 2 , 5 , 8 , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] ]. In Refs.…”

“…The versatility of RF signals extends to various goals, including tissue elasticity imaging, blood flow imaging [ 9 ], radiation force impulse imaging [ 10 ], non-invasive temperature estimation, and tissue characterization [ 11 ]. Importantly, the use of RF TS eliminates the need for modeling tissue or calculating model parameters [ 12 , 13 ].…”

“…Notably, the authors highlighted that their most successful outcomes were achieved by employing six spectral features and incorporating the fractal dimension, using SVM classifiers [ 5 , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] ]. A related study by Aboofazeli et al explored discrete wavelet transform for RF TS analysis but found no significant improvement compared to the spectral and fractal properties [ 12 ]. The impact of US imaging parameters, including transmission power and frame rate, on tissue classification was studied [ 14 ].…”

Ultrasound-Guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study

“…This method has been effectively applied for tissue classification at both high and clinical frequencies [33]. We have also improved the tissue classification results achieved using this method by applying wavelet transform [34], and depth-dependent phase shift of RF time series [35]. The feasibility of this method for ablation region classification was demonstrated in a limited study with three tissue samples [36].…”

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

“…The study shows that the time series features are significantly more sensitive and accurate compared to the texture-based and spectral features proposed in the literature 10,11 for detecting cancerous tissue. In order to improve the classification accuracy between healthy and cancerous specimens of the prostate, Aboofazeli et al used wavelet transform approximation and detail sequences of RF time series of prostate tissue as a feature set 9 . The results show that wavelet features probably cannot be recognized as an alternative feature set for RF time series features.…”