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Related Experiment Video

Updated: May 25, 2026

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface
06:15

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface

Published on: September 15, 2023

Content-based microscopic image retrieval system for multi-image queries.

Hatice Cinar Akakin1, Metin N Gurcan

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. haticecinarakakin@anadolu.edu.tr

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|February 8, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a multitiered content-based image retrieval (CBIR) system for classifying microscopic images of diseases like follicular lymphoma (FL) and neuroblastoma (NB). The novel system significantly improves diagnostic accuracy for disease subtypes using multi-image queries.

Related Experiment Videos

Last Updated: May 25, 2026

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface
06:15

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface

Published on: September 15, 2023

Area of Science:

  • Medical image analysis
  • Computational pathology
  • Information retrieval

Background:

  • Accurate classification of microscopic images is crucial for diagnosing diseases like follicular lymphoma (FL) and neuroblastoma (NB).
  • Distinguishing between subtypes of these diseases from microscopic images presents a significant challenge in pathology.
  • Existing image retrieval systems may lack the specificity required for complex subtyping tasks.

Purpose of the Study:

  • To design and develop a multitiered content-based image retrieval (CBIR) system tailored for microscopic pathology images.
  • To enhance the classification and retrieval of microscopic images, particularly for difficult-to-discriminate disease subtypes.
  • To enable both multi-image query and slide-level retrieval for improved semantic consistency.

Main Methods:

  • Development of a multitiered CBIR system incorporating novel weighting terms inspired by information retrieval theory.
  • Implementation of multi-image query and slide-level retrieval functionalities.
  • Validation of the system on a dataset of 1666 high power fields from follicular lymphoma (FL) and neuroblastoma (NB) tissue slides, annotated by expert pathologists.

Main Results:

  • The proposed CBIR system achieved high classification accuracy at the first rank retrieval: approximately 93% for FL and 86% for NB.
  • The multi-image query algorithm with the new weighting strategy significantly outperformed image-level retrieval accuracy by 38 percentage points for FL and 26 for NB.
  • Leave-one-slide out cross-validation demonstrated the system's robust performance on distinct disease subtypes.

Conclusions:

  • The developed multitiered CBIR system effectively classifies and retrieves microscopic images of FL and NB subtypes.
  • The novel weighting strategy and multi-image query approach enhance diagnostic accuracy in computational pathology.
  • This system offers a promising tool for pathologists, improving the precision of disease subtyping from microscopic data.