Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Left ventricular border recognition using a dynamic search algorithm.

D L Pope, D L Parker, P D Clayton

    Radiology
    |May 1, 1985
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    New Zealand White Rabbits Fed High Cholesterol Diets Develop Morbid Systemic Diseases before Intracranial Atherosclerosis is Detected.

    Journal of veterinary science & medical diagnosis·2026
    Same author

    Improving image quality in transcranial magnetic resonance guided focused ultrasound using a conductive screen.

    Magnetic resonance imaging·2021
    Same author

    Effect of Time Elapsed since Gadolinium Administration on Atherosclerotic Plaque Enhancement in Clinical Vessel Wall MR Imaging Studies.

    AJNR. American journal of neuroradiology·2019
    Same author

    Low-intensity ultrasound activates vestibular otolith organs through acoustic radiation force.

    The Journal of the Acoustical Society of America·2017
    Same author

    Clinical workstations: An architectural prespective.

    Yearbook of medical informatics·2016
    Same author

    The state of clinical information systems after four decades of effort.

    Yearbook of medical informatics·2016
    Same journal

    Erratum for: Prediction of Lobar Emphysema Progression with a CT-Based Foundational Model.

    Radiology·2026
    Same journal

    Erratum for: Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

    Radiology·2026
    Same journal

    Erratum for: Blue Rubber Bleb Nevus Syndrome.

    Radiology·2026
    Same journal

    Redefining the Clinical Role of MRI in Endometrial Cancer Staging.

    Radiology·2026
    Same journal

    To Ablate or Not to Ablate: The Colorectal Liver Metastasis Question.

    Radiology·2026
    Same journal

    The Limits of Radiologic Categorization in Pulmonary Nonsolid Nodules.

    Radiology·2026
    See all related articles

    A novel automated algorithm accurately detects left ventricular boundaries using dynamic programming. This method outperforms previous techniques and highlights differences between subtracted and unsubtracted cardiac images.

    Area of Science:

    • Cardiology
    • Medical Imaging
    • Computer-Aided Diagnosis

    Background:

    • Accurate delineation of left ventricular (LV) boundaries is crucial for cardiac function assessment.
    • Traditional methods often require manual tracing, which can be time-consuming and operator-dependent.
    • Automated algorithms aim to improve efficiency and reproducibility in cardiac image analysis.

    Purpose of the Study:

    • To present initial results of a simple, fully automated algorithm for left ventricular boundary detection.
    • To evaluate the effectiveness of dynamic programming search techniques in border definition.
    • To compare computer-determined borders with manual tracings and assess the impact of image subtraction.

    Main Methods:

    • Development of a fully automated algorithm utilizing dynamic programming search for LV boundary detection.

    Related Experiment Videos

  • Evaluation of the algorithm's performance by comparing computer-determined borders with hand-traced borders.
  • Analysis of borders derived from both subtracted and unsubtracted cardiac images.

    • Main Results:

    • The automated algorithm demonstrated accurate detection of left ventricular boundaries.
    • Dynamic programming allowed local border points to be influenced by the global border location, enhancing accuracy.
    • The automated algorithm performed better than previously described methods requiring operator interaction.
    • Ventricular borders derived from subtracted images differed significantly from those derived from unsubtracted images for both manual and automated methods.

    Conclusions:

    • A simple, fully automated algorithm using dynamic programming effectively detects left ventricular boundaries.
    • This approach offers an improvement over existing algorithms by minimizing operator interaction.
    • Image subtraction significantly impacts ventricular border definition, regardless of the tracing technique used.