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

Estimating fractal dimension with fractal interpolation function models

A I Penn1, M H Loew

  • 1Alan Penn & Associates, Rockville, MD 20850, USA. apenn@math.gwu.edu

IEEE Transactions on Medical Imaging
|April 9, 1998
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

The entry-exit method of shadow boundary segmentation.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

A new impedance imaging system and dipole basis reconstruction method.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2009
Same author

Discrimination of MR images of breast masses with fractal-interpolation function models.

Academic radiology·2000
Same author

Automated registration of brain images using edge and surface features.

IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society·1999
Same author

Use of the fast Hartley transform for three-dimensional dose calculation in radionuclide therapy.

Medical physics·1998
Same author

Issues in multimodality medical image registration.

Journal of digital imaging·1997
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
See all related articles

A new fractal dimension (fd) estimation method using fractal interpolation functions (FIFs) improves medical image texture analysis. This approach offers better class separation for medical images compared to box-counting and power spectrum methods.

Area of Science:

  • Medical Imaging Analysis
  • Computational Anatomy
  • Biophysical Characterization

Background:

  • Fractal dimension (fd) is crucial for characterizing medical image texture and separating image classes.
  • Existing methods like box-counting (BC) and power spectrum (PS) have limitations with low-resolution data and model applicability.

Purpose of the Study:

  • To analyze limitations of current fractal dimension estimation methods (BC and PS).
  • To introduce a novel fractal dimension estimation method using fractal interpolation functions (FIFs).
  • To evaluate the new method's efficacy in characterizing medical image fractal texture.

Main Methods:

  • Investigated limitations of box-counting (BC) and power spectrum (PS) methods for fractal dimension estimation.
  • Developed a new method constructing multiple fractal interpolation function (FIF) models.

Related Experiment Videos

  • Calculated the mean of FIF-derived fractal dimensions as the primary estimate and standard deviation as a confidence measure.

    • Main Results:

    • The new FIF-based method demonstrated improved separation of medical image classes.
    • This method showed enhanced characterization of fractal texture in medical images.
    • A pilot study on red blood cell images showed superior class separation compared to BC and PS.

    Conclusions:

    • The novel FIF-based method provides a more robust and reliable estimation of fractal dimension for medical images.
    • This technique offers improved confidence measures for fractal dimension estimates.
    • The method shows significant potential for advancing medical image analysis and texture characterization.