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

Classification of Systems-II01:31

Classification of Systems-II

240
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
240
Classification of Systems-I01:26

Classification of Systems-I

293
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
293
Methods of Classification and Identification01:28

Methods of Classification and Identification

177
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
177
Aggregates Classification01:29

Aggregates Classification

376
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
376
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.5K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
7.5K
Classification of Leukocytes01:30

Classification of Leukocytes

2.6K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Complementary analysis of pristine, UV-aged and extracted microplastics using single particle ICP-MS and OF2i-Raman spectroscopy.

Talanta·2026
Same author

Optical Extraction of Single Microplastics Followed by Online Molecular and Elemental Characterization.

Analytical chemistry·2026
Same author

Assessing Particle Release from Intraocular Lenses with a Combination of OptoFluidic Force Induction, μ-Raman and μ-FTIR.

Bioengineering (Basel, Switzerland)·2025
Same author

Nd:YAG Laser Induced Microfragmentation in Intraocular Lenses: A Correlative Optical and Raman Spectroscopy Study.

Current eye research·2025
Same author

Revealing the Hidden Polysulfides in Solid-State Na-S Batteries: How Pressure and Electrical Transport Control Kinetic Pathways.

Journal of the American Chemical Society·2025
Same author

Electrochemically Induced Nanoscale Stirring Boosts Functional Immobilization of Flavocytochrome P450 BM3 on Nanoporous Gold Electrodes.

Small methods·2024

Related Experiment Video

Updated: Sep 7, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Enhancing classification in correlative microscopy using multiple classifier systems with dynamic selection.

Samuel Bitrus1, Harald Fitzek2, Eugen Rigger1

  • 1Department of Digital Engineering, V-Research GmbH, Stadtstraße 33, Dornbirn 6850, Austria.

Ultramicroscopy
|June 19, 2022
PubMed
Summary

Automated classification using machine learning significantly enhances correlative microscopy by accurately analyzing complex sample data. Multiple classifier systems achieved 99% accuracy, proving their value in scientific discovery.

Keywords:
ClassificationCorrelative microscopyDynamic selectionMachine learningMultiple classifier systems

More Related Videos

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

14.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Related Experiment Videos

Last Updated: Sep 7, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

14.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Area of Science:

  • Materials Science
  • Geology
  • Computational Science

Background:

  • Correlative microscopy integrates data from diverse microscopic techniques for comprehensive specimen analysis.
  • Accurate classification of combined analytical signals into meaningful phases is crucial but challenging due to imbalanced data and high intra-class variability.
  • Existing research lacks studies on classifier performance specifically within the context of correlative microscopy.

Purpose of the Study:

  • To investigate the efficacy of single and multiple classifier systems for automated data analysis in correlative microscopy.
  • To evaluate classifier performance on a complex geological sample using multi-modal data.
  • To demonstrate the potential of machine learning for phase identification in correlative microscopy.

Main Methods:

  • Utilized a volcanic rock sample with data acquired from Raman spectroscopy, Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDS).
  • Prepared multi-modal data for algorithmic evaluation.
  • Compared the performance of single classifiers against multiple classifier systems employing dynamic selection.

Main Results:

  • Multiple classifier systems significantly outperformed single classifiers in classifying the correlative microscopy data.
  • Achieved a high Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) of 99%.
  • Demonstrated the successful application of automated classification for analyzing complex, multi-modal datasets.

Conclusions:

  • Automated classification, particularly using multiple classifier systems, is highly applicable and effective in correlative microscopy.
  • The study highlights the synergistic potential of combining correlative microscopy and machine learning research fields.
  • This approach offers a robust solution for handling challenges like imbalanced classes and high intra-class variability in real-world applications.