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

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Aboofazeli, M., Abolmaesumi, P., Moradi, M., Sauerbrei, E., Siemens, R., Boag, A., & Mousavi, P. (2009). Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series. Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 7260, 72603J. https://doi.org/10.1117/12.813831

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Author(s)

Aboofazeli, Mohammad
Abolmaesumi, Purang
Moradi, Mehdi
Sauerbrei, Eric
Siemens, Robert
Boag, Alexander
Mousavi, Parvin

Title

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

Publication Type

article

Date issued

2009

Journal/Book/Host Title

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis

Volume

7260

Start Page

72603J

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. [ABSTRACT FROM AUTHOR]

DOI

10.1117/12.813831

ISSN

0277-786X

Publication note

Final article published

Language

English

Publisher

Society of Photo-optical Instrumentation Engineers (SPIE)

Peer reviewed?

Peer reviewed

Use rights

http://rightsstatements.org/vocab/InC-EDU/1.0/
©2009. SPIE.