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

New prospects in CT image processing via mathematical morphology.

F Prêteux, A M Laval-Jeantet, B Roger

    European Journal of Radiology
    |November 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

    Dynamic Physical Constraints: Emulating Hard Surfaces with High Realism.

    IEEE transactions on haptics·2016
    Same author

    Multicentre European COMAC-BME study on the standardisation of bone densitometry procedures.

    Technology and health care : official journal of the European Society for Engineering and Medicine·2014
    Same author

    Excellent reliability for MRI grading and prognostic parameters in acute hamstring injuries.

    British journal of sports medicine·2013
    Same author

    Characterisation and quantification of flavonoids in Iris germanica L. and Iris pallida Lam. resinoids from Morocco.

    Phytochemical analysis : PCA·2012
    Same author

    Influence of abutment design on the success of immediately loaded dental implants: experimental and numerical studies.

    Medical engineering & physics·2011
    Same author

    Adenylyl cyclase 8 is central to glucagon-like peptide 1 signalling and effects of chronically elevated glucose in rat and human pancreatic beta cells.

    Diabetologia·2010

    Mathematical morphology (M.M.) enhances CT image analysis by automating organ extraction for precise densitometry. This advanced technique improves texture analysis and pattern recognition in medical imaging.

    Area of Science:

    • Medical Imaging
    • Image Processing
    • Computational Anatomy

    Background:

    • Traditional CT densitometry relies on manual Region of Interest (ROI) selection, introducing approximations and reducing precision.
    • Mathematical Morphology (M.M.) offers a set-theoretic approach using structuring elements for neighborhood transformations, enabling advanced texture analysis and pattern recognition.

    Purpose of the Study:

    • To explore the application of Mathematical Morphology (M.M.) in Computed Tomography (CT) image processing for enhanced quantitative analysis.
    • To demonstrate M.M.'s capability in automating organ segmentation and improving the accuracy of densitometry measurements.

    Main Methods:

    • Applied M.M. to automatically isolate vertebral bodies in thoracic and abdominal CT slices for bone density quantification.
    • Utilized morphomathematical analysis on metacarpal scans to quantify bone evolution parameters, including cortical density and medullar area.

    Related Experiment Videos

  • Employed M.M. for automatic segmentation of lung parenchyma to measure mean density and assess vascular network contribution.

    • Main Results:

    • Achieved automatic extraction of organs from background, eliminating manual ROI selection errors and improving precision, reliability, and reproducibility.
    • Quantified vertebral bone density (trabecular and cortical) and bone evolution parameters (cortical density, cortico-diaphyseal index, medullar area) in metacarpals.
    • Successfully segmented lung parenchyma, measured its mean density, and determined the relative importance of the vascular network.

    Conclusions:

    • Mathematical Morphology (M.M.) provides a robust framework for advanced CT image processing, offering significant improvements over traditional methods.
    • The automated segmentation and quantification capabilities of M.M. enhance the precision and reliability of diagnostic assessments in various anatomical regions.
    • M.M. presents new prospects for quantitative analysis in medical imaging, particularly in bone densitometry and lung parenchyma evaluation.