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. [ABSTRACT FROM AUTHOR]
This paper presents a novel method for tissue characterization using wavelet transform of ultrasound radio frequency (RF) echo signals. We propose the use of multiscale products of wavelet transform sequences of RF echoes to estimate the scatterer distribution in the tissue. The proposed method is based on the fact that when emitted ultrasound beams interact with scatterers in the tissue, backscattered beams contain singularities corresponding to the location of the scatterers. The singularities will exist in multiple scales of wavelet sequences of the echo signals. Therefore, peaks of wavelet transform multiscale products correspond to the location of scatterers. Estimation of scatterer spacing can be used for tissue characterization. The efficacy of the proposed method was validated in RF echo signals of in-vitro human prostate to characterize normal and cancerous tissue. The results confirm that wavelet transform multiscale products of RF echo signals contain tissue typing information that can be used as an effective tool to differentiate normal and cancerous prostate tissue.
Ultrasound image guided needle insertion is the method of choice for a wide variety of medical diagnostic and therapeutic procedures. When flexible needles are inserted in soft tissue, these needles generally follow a curved path. Segmenting the trajectory of the needles in ultrasound images will facilitate guiding them within the tissue. In this paper, a novel algorithm for curved needle segmentation in three-dimensional (3D) ultrasound images is presented. The algorithm is based on the projection of a filtered 3D image onto a two-dimensional (2D) image. Detection of the needle in the resulting 2D image determines a surface on which the needle is located. The needle is then segmented on the surface. The proposed technique is able to detect needles without any previous assumption about the needle shape, or any a priori knowledge about the needle insertion axis line.
