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Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals

Bibliographic Details
Title: Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals
Authors: Noushin R. Farnoud
Publication Year: 2004
Subject Terms: Data analytics and signal processing, n.e.c, Signal processing -- Computer simulation, Backscattering -- Measurement, Diagnostic imaging -- Digital techniques, Apoptosis -- Research -- Methodology
Description: In this study, we explore the possibility of monitoring program cell death (apoptosis) and classifying clusters of apoptotic cells based on the changes in high frequency ultrasound backscatter signals from these cells. One of the hallmarks of cancer is that the fail [sic] in the apoptosis mechanism in cells. Therefore this research carries the promise of designing more refined and more effective cancer therapies. The ultrasound signals are modeled through the Autoregressive (AR) modeling technique. The proper model order is calculated by tracking the error criteria derived from statistical properties of the original and modeled signal. In the next stage, five machine learning classifiers are developed to classify backscatter signals based on their AR coefficients. In clinical applications ultrasound backscatter signals from tissues and tumors are most likely to be non-stationary. Therefore analyzing such signals requires signal segmentation techniques. We developed recursive least square lattice filter for adaptive segmentation of ultrasound backscatter signals from multiple cell types into blocks of stationary segments, and model and classify the segments individually. In this thesis we demonstrate the accuracy of modeling, segmentation and classification techniques to detect signals from different cell pellets based on the signal processing and machine learning techniques.
Document Type: thesis
Language: unknown
DOI: 10.32920/ryerson.14643777.v1
Availability: https://doi.org/10.32920/ryerson.14643777.v1
https://figshare.com/articles/thesis/Design_and_Application_of_Signal_Modeling_Segmentation_and_Classification_Methods_for_High-Frequency_Ultrasound_Backscatter_Signals/14643777
Rights: In Copyright
Accession Number: edsbas.DB27C269
Database: BASE
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  – Url: https://doi.org/10.32920/ryerson.14643777.v1#
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PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 685.344299316406
IllustrationInfo
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  Label: Title
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  Data: Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Noushin+R%2E+Farnoud%22">Noushin R. Farnoud</searchLink>
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  Label: Publication Year
  Group: Date
  Data: 2004
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  Data: <searchLink fieldCode="DE" term="%22Data+analytics+and+signal+processing%22">Data analytics and signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22n%2Ee%2Ec%22">n.e.c</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing+--+Computer+simulation%22">Signal processing -- Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Backscattering+--+Measurement%22">Backscattering -- Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging+--+Digital+techniques%22">Diagnostic imaging -- Digital techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Apoptosis+--+Research+--+Methodology%22">Apoptosis -- Research -- Methodology</searchLink>
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  Label: Description
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  Data: In this study, we explore the possibility of monitoring program cell death (apoptosis) and classifying clusters of apoptotic cells based on the changes in high frequency ultrasound backscatter signals from these cells. One of the hallmarks of cancer is that the fail [sic] in the apoptosis mechanism in cells. Therefore this research carries the promise of designing more refined and more effective cancer therapies. The ultrasound signals are modeled through the Autoregressive (AR) modeling technique. The proper model order is calculated by tracking the error criteria derived from statistical properties of the original and modeled signal. In the next stage, five machine learning classifiers are developed to classify backscatter signals based on their AR coefficients. In clinical applications ultrasound backscatter signals from tissues and tumors are most likely to be non-stationary. Therefore analyzing such signals requires signal segmentation techniques. We developed recursive least square lattice filter for adaptive segmentation of ultrasound backscatter signals from multiple cell types into blocks of stationary segments, and model and classify the segments individually. In this thesis we demonstrate the accuracy of modeling, segmentation and classification techniques to detect signals from different cell pellets based on the signal processing and machine learning techniques.
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  Data: 10.32920/ryerson.14643777.v1
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  Data: https://doi.org/10.32920/ryerson.14643777.v1<br />https://figshare.com/articles/thesis/Design_and_Application_of_Signal_Modeling_Segmentation_and_Classification_Methods_for_High-Frequency_Ultrasound_Backscatter_Signals/14643777
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        Value: 10.32920/ryerson.14643777.v1
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    Subjects:
      – SubjectFull: Data analytics and signal processing
        Type: general
      – SubjectFull: n.e.c
        Type: general
      – SubjectFull: Signal processing -- Computer simulation
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      – SubjectFull: Backscattering -- Measurement
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      – SubjectFull: Diagnostic imaging -- Digital techniques
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      – SubjectFull: Apoptosis -- Research -- Methodology
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      – TitleFull: Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals
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