PURPOSE: A ground-truth validation platform was developed to provide spatial correlation between ultrasound
(US), temperature measurements and histopathology images to validate US based thermal ablation monitoring
methods. METHOD: The test-bed apparatus consists of a container box with integrated fiducial lines. Tissue
samples are suspended within the box using agar gel as the fixation medium. Following US imaging, the gel block
is sliced and pathology images are acquired. Interactive software segments the fiducials as well as structures
of interest in the pathology and US images. The software reconstructs the regions in 3D space and performs
analysis and comparison of the features identified from both imaging modalities. RESULTS: The apparatus and
software were constructed to meet technical requirements. Tissue samples were contoured, reconstructed and
registered in the common coordinate system of fiducials. There was agreement between the sample shapes, but
systematic shift of several millimeters was found between histopathology and US. This indicates that during
pathology slicing shear forces tend to dislocate the fiducial lines. Softer fiducial lines and harder gel material
can eliminate this problem. CONCLUSION: Viability of concept was presented. Despite our straightforward
approach, further experimental work is required to optimize all materials and customize software.
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
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.