Prostate Cancer Probability Maps Based on Ultrasound RF Time Series and SVM Classifiers

Moradi, Mehdi; Mousavi, Parvin; Siemens, Robert; Sauerbrei, Eric E.; Boag, Alexander H.; Abolmaesumi, Purang

doi:10.1007/978-3-540-85988-8_10

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…It has been shown that the fractal dimension of the RF time series computed based on Higuchi's algorithm [25] contains tissue typing information [23]. We computed the FD (Kmax = 16) of all the time series within an ROI and averaged them to get one feature per ROI.…”

Section: Fractal Dimension (Fd)mentioning

confidence: 99%

“…Additionally, a regression line was fitted to the values of the spectrum versus normalized frequency plot. The intercept (S5) and the slope (S6) of this line were used as two more features [23].…”

Section: Spectral Featuresmentioning

confidence: 99%

“…Six spectral features were extracted as proposed by Moradi et al [23]. Frequency spectrum of each time series was calculated after removing the mean, and zero-padding of the length of the time series to 128.…”

Section: Spectral Featuresmentioning

confidence: 99%

“…RF time series have been used for tissue typing in animal tissues [22]. The performance of RF time series features for detection of prostate cancer has been evaluated in a previous study [23]. Quantitative comparisons between the RF time series features, and RF echo signal and texture features showed that the RF time series features are considerably more accurate and sensitive compared to the other two categories of features.…”

Section: Introductionmentioning

confidence: 98%

“…Using these probability values, cancer probability maps were generated. The effects of imaging depth, acoustic power and imaging frame rate on the tissue typing capabilities of RF time series were studied [23].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2009

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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.

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Section: Fractal Dimension (Fd)mentioning

confidence: 99%

Section: Spectral Featuresmentioning

confidence: 99%

Section: Spectral Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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

et al. 2009

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Tissue characterization using multiscale products of wavelet transform of ultrasound radio frequency echoes

Aboofazeli

Abolmaesumi

Fichtinger

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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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.

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Early differentiating between the chemotherapy responders and nonresponders: preliminary results with ultrasonic spectrum analysis of the RF time series in preclinical breast cancer models

Huang

Wang

et al. 2019

Cancer Imaging

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Background This study was aimed to assess whether ultrasonic spectrum analysis of radiofrequency (RF) time series using a clinical ultrasound system allows for early differentiating between the chemotherapy responders and nonresponders in human breast cancer xenografts that imitate clinical responding and nonresponding tumors. Methods Clinically responding ( n = 20; MCF-7) and nonresponding ( n = 20; MBA-MD-231) breast cancer xenografts were established in 40 nude mice. Ten mice from each group received either chemotherapy (adriamycin, 4 mg/kg) or saline as controls. Each tumor was imaged longitudinally with a clinical ultrasound scanner at baseline (day 0) and subsequently on days 2, 4, 6, 8 and 12 following treatment, and the corresponding RF time-series data were collected. Changes in six RF time-series parameters ( slope , intercept , S1 , S2 , S3 and S4 ) were compared with the measurement of the tumor cell density, and their differential performances of the treatment response were analyzed. Results Adriamycin significantly inhibited tumor growth and decreased the cancer cell density in responders ( P < 0.001) but not in nonresponders ( P > 0.05). Fold changes of s lope were significantly increased in responders two days after adriamycin treatment ( P = 0.002), but not in nonresponders ( P > 0.05). Early changes in slope on day 2 could differentiate the treatment response in 100% of both responders (95% CI, 62.9–100.0%) and nonresponders (95% CI, 88.4–100%). Conclusions Ultrasonic RF time series allowed for the monitoring of the tumor response to chemotherapy and could further serve as biomarkers for early differentiating between the treatment responders and nonresponders. Electronic supplementary material The online version of this article (10.1186/s40644-019-0248-y) contains supplementary material, which is available to authorized users.

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Prostate Cancer Probability Maps Based on Ultrasound RF Time Series and SVM Classifiers

Cited by 3 publications

References 10 publications

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

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

Tissue characterization using multiscale products of wavelet transform of ultrasound radio frequency echoes

Early differentiating between the chemotherapy responders and nonresponders: preliminary results with ultrasonic spectrum analysis of the RF time series in preclinical breast cancer models

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