Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

Imani, Farhad; Daoud, Mohammad I.; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

doi:10.1117/12.877845

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The average spectrum is then normalized by dividing it through its maximum. Ten features, previously reported by our group for tissue characterization, are extracted from the RF time series signal and summarized in Table I [33], [35]- [37]. Features include the sums of the power spectrum in four frequency bands (Features 1-4) depicted in Fig.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Central frequency as a function of depth (CF(z)) is defined for each spatial sample in the RF data as the mean of bandwidth of the power spectrum of its corresponding time series [35]:…”

Section: Feature Extractionmentioning

confidence: 99%

“…This method has been effectively applied for tissue classification at both high and clinical frequencies [33]. We have also improved the tissue classification results achieved using this method by applying wavelet transform [34], and depth-dependent phase shift of RF time series [35]. The feasibility of this method for ablation region classification was demonstrated in a limited study with three tissue samples [36].…”

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confidence: 95%

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Ultrasound-Guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study

Imani

Abolmaesumi

Lassó

et al. 2013

IEEE Trans. Biomed. Eng.

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This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and nonablated tissue. For 12 ex vivo and two in situ tissue samples, RF ultrasound signals are acquired prior to, and following, high-intensity ultrasound ablation. Spatial and temporal features of these signals are used to characterize ablated and nonablated tissue in a supervised-learning framework. In cross-validation evaluation, a subset of four features extracted from RF time series produce a classification accuracy of 84.5%, an area under ROC curve of 0.91 for ex vivo data, and an accuracy of 85% for in situ data. Ultrasound RF time series is a promising approach for characterizing ablated tissue.

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Section: Feature Extractionmentioning

confidence: 99%

“…Central frequency as a function of depth (CF(z)) is defined for each spatial sample in the RF data as the mean of bandwidth of the power spectrum of its corresponding time series [35]:…”

Section: Feature Extractionmentioning

confidence: 99%

mentioning

confidence: 95%

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Ultrasound-Guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study

Imani

Abolmaesumi

Lassó

et al. 2013

IEEE Trans. Biomed. Eng.

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“…TeUS has been also used for characterizing in vitro animal tissues [19], [24], [25]. Ana-lyzing temporal ultrasound sequences is a promising technique to augment biopsy procedures with tissue-specific information for guiding the needle to areas that are highly likely to be malignant.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization

Nahlawi

Goncalves

Imani

et al. 2018

IEEE Trans. Biomed. Eng.

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Objectives: Temporal Enhanced Ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS – temporal order and length – and present a new framework to assess their impact on tissue information. Methods: We utilize a probabilistic modeling approach using Hidden Markov Models (HMMs) to capture the temporal signatures of malignant and benign tissues from TeUS signals of 9 patients. We model signals of benign and malignant tissues (284 and 286 signals, respectively) in their original temporal order as well as under order permutations. We then compare the resulting models using the Kullback-Liebler divergence and assess their performance differences in characterization. Moreover, we train HMMs using TeUS signals of different durations and compare their model performance when differentiating tissue types. Results: Our findings demonstrate that models of order-preserved signals perform statistically significantly better (85% accuracy) in tissue characterization compared to models of order-altered signals (62% accuracy). The performance degrades as more changes in signal-order are introduced. Additionally, models trained on shorter sequences perform as accurately as models of longer sequences. Conclusion: The work presented here strongly indicates that temporal order has substantial impact on TeUS performance, thus it plays a significant role in conveying tissue-specific information. Further-more, shorter TeUS signals can relay sufficient information to accurately distinguish between tissue types. Significance: Under-standing the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition.

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Ultrasound tissue classification: a review

et al. 2020

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Ultrasound imaging is the most widespread medical imaging modality for creating images of the human body in clinical practice. Tissue classification in ultrasound has been established as one of the most active research areas, driven by many important clinical applications. In this paper, we present a survey on ultrasound tissue classification, focusing on recent advances in this area. We start with a brief review on the main clinical applications. We then introduce the traditional approaches, where the existing research on feature extraction and classifier design are reviewed. As deep learning approaches becoming popular for medical image analysis, the recent deep learning methods for tissue classification are also introduced. We briefly discuss the FDA-cleared techniques being used clinically. We conclude with the discussion on the challenges and research focus in future.

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Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

Cited by 7 publications

References 8 publications

Ultrasound-Guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study

Ultrasound-Guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study

Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization

Ultrasound tissue classification: a review

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