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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.
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Immunocytochemistry and Immunohistochemistry01:22

Immunocytochemistry and Immunohistochemistry

Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
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Immunofluorescence Microscopy01:12

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ER Retrieval Pathway01:45

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X-ray Imaging01:24

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In-situ Hybridization02:31

In-situ Hybridization

In situ hybridization (ISH) is a technique used to detect and localize specific DNA or RNA molecules in cells, tissue, or tissue sections using a labeled probe. The technique was first used in 1969 for the investigation of nucleic acids. It is currently an essential tool in scientific research and clinical settings, especially for diagnostic purposes.
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Related Experiment Video

Updated: Jul 1, 2026

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment
06:32

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Published on: August 18, 2023

Localized content-based image retrieval.

Rouhollah Rahmani1, Sally A Goldman, Hui Zhang

  • 1One Microsoft Way, Redmond, WA 98052, USA. rouholr@microsoft.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 13, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces Accio, a localized content-based image retrieval (CBIR) system. Accio precisely identifies relevant image portions for more accurate image searching and database ranking.

Related Experiment Videos

Last Updated: Jul 1, 2026

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment
06:32

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Published on: August 18, 2023

Area of Science:

  • Computer Science
  • Information Retrieval
  • Artificial Intelligence

Background:

  • Traditional content-based image retrieval (CBIR) systems often struggle with user queries focused on specific image regions.
  • Existing methods lack the ability to effectively filter irrelevant parts of an image during retrieval.

Purpose of the Study:

  • To develop a novel localized content-based image retrieval (CBIR) system named Accio.
  • To address the challenge of representing image content for retrieval tasks where only a portion of the image is of interest.

Main Methods:

  • Accio utilizes a multiple-instance learning algorithm with labeled images to identify and weight relevant features.
  • The system employs a similarity measure focused exclusively on pertinent image sections for ranking.
  • Two new image representation techniques, extending segmentation-based and salient point-based methods, are presented and compared.

Main Results:

  • The developed system, Accio, demonstrates effective localized image retrieval by focusing on relevant image content.
  • The novel image representations show promise in capturing essential features for localized CBIR.
  • The multiple-instance learning approach successfully identifies and prioritizes desired objects within images.

Conclusions:

  • Localized CBIR is a viable and important task, requiring specialized approaches beyond traditional CBIR.
  • Accio provides a robust framework for localized image retrieval, improving accuracy by focusing on relevant image content.
  • Further research into image representation techniques is crucial for advancing localized CBIR systems.