Computer-Aided Diagnosis of Prostate Cancer with Emphasis on Ultrasound-Based Approaches: A Review

Moradi, Mehdi; Mousavi, Parvin; Abolmaesumi, Purang

doi:10.1016/j.ultrasmedbio.2007.01.008

Cited by 58 publications

(33 citation statements)

References 152 publications

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“…Finally, the values of the parameters in (8) have to be chosen. We optimized the parameters by registering data sets for five patients and visually inspect the results.…”

Section: Elastic Surface Registrationmentioning

confidence: 99%

“…5 Given the limits of current diagnostic and treatment methods, new ultrasound-based prostate cancer localization techniques, like elastography and contrast enhanced ultrasound, are emerging to enable targeted biopsy and focal therapy. [6][7][8] However, accurate validation of these methods is required prior to introduction into clinical practice. Due to the lack of imaging techniques revealing the exact location of PCa, histopathologic analysis of the prostate after RP is frequently used as gold standard for validation.…”

Section: Introductionmentioning

confidence: 99%

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3D registration of histology and ultrasound data for validation of prostate cancer imaging

et al. 2013

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Several ultrasound (US) prostate cancer localization methods are emerging, opening opportunities for targeted biopsies and focal therapy. However, before any of these methods, like elastography or contrast-enhanced US, can be introduced into clinical practice, accurate validation is required. The current gold standard for validation is histological assessment of the prostate after radical prostatectomy. Therefore, a 3D registration of histological and US data is required. This task is complicated by misalignment between histology slices and ultrasound imaging planes, pressure caused by the adopted transrectal US probe, and deformation and volume change during fixation in formalin solution. In this work, we introduce a dedicated 3D algorithm, automatically registering histology and ultrasound data. Because there is no information available between histology slices, and internal landmarks are not consistently present in US images, registration is based on outer-contours shape only. A 3D surface model of the prostate in constructed, based on manually outlined contours in a transrectal sweep video and a longitudinal image. Also, a similar model is constructed from the histology slices, including cancerous areas marked by a pathologist. Registration of the models is then performed in three steps: affine registration, elastic surface registration, and internal registration. In-vitro validation of the algorithm was performed by inserting rubber wires into four prostate mimicking phantoms and applying probe pressure. The resulting registration accuracy was 1.6 mm, which is considerably smaller than the histology slicing resolution of 4 mm.

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“…Finally, the values of the parameters in (8) have to be chosen. We optimized the parameters by registering data sets for five patients and visually inspect the results.…”

Section: Elastic Surface Registrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D registration of histology and ultrasound data for validation of prostate cancer imaging

et al. 2013

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“…Numerous techniques have been proposed for computer assisted detection of prostate cancer tumors in TRUS images [11]. The features used for TRUS image-based detection of PC may be divided into three distinct categories:…”

Section: Introductionmentioning

confidence: 99%

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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“…There is a wealth of literature on ultrasound-based diagnosis of prostate cancer [2]. Texture features of B-scan images and spectral features (Lizzi-Feleppa features [3]) extracted from calibrated average spectrum of RF signals have been used along with numerous classification approaches for tissue typing [4].…”

Section: Introductionmentioning

confidence: 99%

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

Moradi¹,

Mousavi²,

Siemens³

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. We describe a very efficient method based on ultrasound RF time series analysis and support vector machine classification for generating probabilistic prostate cancer colormaps to augment the biopsy process. To form the RF time series, we continuously record ultrasound RF echoes backscattered from tissue while the imaging probe and the tissue are stationary in position. In an in-vitro study involving 30 prostate specimens, we show that the features extracted from RF time series are significantly more accurate and sensitive compared to two other established categories of ultrasound-based tissue typing methods. The method results in an area under ROC curve of 0.95 in 10-fold cross-validation.

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Computer-Aided Diagnosis of Prostate Cancer with Emphasis on Ultrasound-Based Approaches: A Review

Cited by 58 publications

References 152 publications

3D registration of histology and ultrasound data for validation of prostate cancer imaging

3D registration of histology and ultrasound data for validation of prostate cancer imaging

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

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

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