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

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

You might also read

Related Articles

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

Sort by
Same author

Multi-label Ensemble Model for Knee Joint Anatomy and Lesion Segmentation: Segmentation of Clinical Images With Ensembling, Morphology, and Attention (SCEMA).

Magnetic resonance in medicine·2026
Same author

Masked-speech Recognition Using Human and Synthetic Cloned Speech.

Trends in hearing·2025
Same author

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models.

Bioengineering (Basel, Switzerland)·2024
Same author

Special Issue on Healthcare Knowledge Discovery and Management.

Journal of healthcare informatics research·2022
Same author

Extracting chemical-protein relations using attention-based neural networks.

Database : the journal of biological databases and curation·2018
Same author

Intervertebral disc detection in X-ray images using faster R-CNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2017

Related Experiment Video

Updated: Jun 18, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Content based sub-image retrieval system for high resolution pathology images using salient interest points.

Neville Mehta1, Raja' S Alomari, Vipin Chaudhary

  • 1Department of Computer Science and Engineering University at Buffalo, NY 14260, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary

This study introduces a sub-image content-based image retrieval (sCBIR) system for digital pathology. The sCBIR framework efficiently retrieves specific structures from large pathology images, achieving an 80% match with manual searches.

More Related Videos

Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software
07:32

Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software

Published on: April 12, 2024

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Related Experiment Videos

Last Updated: Jun 18, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software
07:32

Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software

Published on: April 12, 2024

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Area of Science:

  • Digital pathology
  • Medical image analysis
  • Computer-aided diagnosis

Background:

  • Digital pathology images are large, necessitating sub-image retrieval for clinical utility.
  • Pathologists need to retrieve specific structures and associated diagnoses from image databases.

Purpose of the Study:

  • To propose a content-based sub-image retrieval (sCBIR) framework for high-resolution digital pathology images.
  • To develop an efficient and robust system for indexing and querying sub-images.

Main Methods:

  • Utilized scale-invariant feature extraction for robust feature representation.
  • Developed an efficient searching mechanism for indexing and query execution.
  • Implemented and tested a working sCBIR system on pathology image datasets.

Main Results:

  • The sCBIR system demonstrated effective retrieval of specific structures of interest.
  • Comparison with manual search showed an 80% match in the top five retrieved sub-images.
  • The system facilitates efficient access to relevant information within large digital pathology archives.

Conclusions:

  • The proposed sCBIR framework is effective for retrieving specific structures in digital pathology.
  • The system offers a significant improvement in efficiency and accuracy compared to manual search methods.
  • This technology has the potential to enhance diagnostic workflows in digital pathology.