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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Automated image segmentation method to analyse skeletal muscle cross section in exercise-induced regenerating myofibers.

Scientific reports·2021
Same author

Automatic Evaluation of Crown Preparation Using Image Processing Techniques: A Substitute to Faculty Scoring in Dental Education.

Journal of medical signals and sensors·2021
Same author

RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

IEEE transactions on medical imaging·2019
Same author

OCT Fluid Segmentation using Graph Shortest Path and Convolutional Neural Network<sup>.</sup>

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2018
Same author

Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain.

PloS one·2017
Same author

Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms.

IEEE transactions on bio-medical engineering·2017

Related Experiment Video

Updated: Nov 8, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.6K

A Framework for Hexagonal Image Processing Using Hexagonal Pixel-Perfect Approximations in Subpixel Resolution.

Sadegh Fadaei, Abdolreza Rashno

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 20, 2021
    PubMed
    Summary

    This study introduces a novel software approach for converting square pixel images to hexagonal lattice images, overcoming challenges in shape, intensity, and resolution. This hexagonal image processing method enhances image quality and performance in various applications.

    More Related Videos

    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.6K
    Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
    14:09

    Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

    Published on: April 7, 2014

    15.9K

    Related Experiment Videos

    Last Updated: Nov 8, 2025

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 12, 2014

    8.6K
    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.6K
    Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
    14:09

    Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

    Published on: April 7, 2014

    15.9K

    Area of Science:

    • Digital Image Processing
    • Computer Vision
    • Lattice Theory

    Background:

    • Hexagonal lattices offer advantages over square lattices in image processing applications like binarization, rotation, scaling, and edge detection.
    • Current hardware limitations necessitate software solutions for converting square pixel data to hexagonal structures to leverage hexagonal lattice benefits.
    • Existing methods face challenges with imperfect hexagonal shapes, inaccurate pixel intensity levels, and reduced resolution in hexagonal spaces.

    Purpose of the Study:

    • To develop a software-based hexagonal platform that effectively converts square pixels to hexagonal ones.
    • To address and resolve key challenges in hexagonal image processing: shape accuracy, intensity fidelity, and resolution.
    • To provide a robust interpolation method for seamless square-to-hexagonal pixel conversion.

    Main Methods:

    • A novel interpolation technique is proposed, calculating hexagonal pixel intensities based on the mathematical formulation of overlaps between square and hexagonal pixels.
    • The method automatically detects and classifies 8 distinct overlap cases between square and hexagonal pixels.
    • The proposed scheme is evaluated using both synthetic and real-world images under varying noise conditions (10 levels).

    Main Results:

    • The proposed interpolation method achieves high accuracy in square-to-hexagonal conversion, demonstrated by a Figure of Merit (FOM) of 99.92% for high SNR and 98.67% for low SNR in synthetic images.
    • Evaluations on real images show superior performance compared to existing hexagonal lattice methods, with an Interclass Correlation Coefficient (ICC) of 84.18% and a mean rating of 7.7 out of 9.
    • The method effectively addresses challenges related to shape, intensity, and resolution in hexagonal image processing.

    Conclusions:

    • The developed hexagonal platform provides a viable software solution for harnessing the benefits of hexagonal image processing.
    • The proposed interpolation method is mathematically sound and experimentally validated for accurate square-to-hexagonal pixel conversion.
    • This approach significantly enhances image processing quality and performance, particularly in interpolation and edge detection tasks.