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Related Experiment Video

Updated: Jul 16, 2026

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart
11:50

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Published on: July 9, 2010

Myocardial tissue characterization using pattern recognition procedures on backscattered ultrasonic signals.

R A Dyer, S A Dyer, P K Bhagat

    Ultrasonic Imaging
    |July 1, 1986
    PubMed
    Summary

    Pattern recognition accurately classified canine and human heart tissue using signal analysis. Incorporating phase information with nearest-neighbor classification improved diagnostic accuracy for both species.

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    Last Updated: Jul 16, 2026

    High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart
    11:50

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    Published on: July 9, 2010

    Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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    Ultrasonic Assessment of Myocardial Microstructure

    Published on: January 14, 2014

    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Research
    • Computational Pathology

    Background:

    • Distinguishing normal from abnormal heart tissue is crucial for diagnosis.
    • In vitro analysis of biological samples offers a controlled environment for developing diagnostic tools.

    Purpose of the Study:

    • To apply pattern recognition techniques to in vitro canine and human heart samples.
    • To evaluate the effectiveness of different feature extraction and classification methods for tissue analysis.

    Main Methods:

    • Backscattered signals from normal and abnormal heart samples (canine and human) were analyzed.
    • Orthogonal transforms and variance criteria were used for feature extraction.
    • Minimum-distance (MD) and nearest-neighbor (NN) classification rules were employed.

    Main Results:

    • The magnitude of the Discrete Fourier Transform (DFT) performed best with the MD rule for both species.
    • All transforms performed comparably with the NN rule.
    • Classification accuracy improved for both species when the NN rule utilized feature extractors with phase information.

    Conclusions:

    • Pattern recognition, particularly with phase information and NN classification, shows promise for analyzing heart tissue.
    • The developed methods demonstrate potential for differentiating normal and abnormal cardiac samples in both canine and human subjects.