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Updated: Jun 17, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Wavelet optimization for content-based image retrieval in medical databases.

G Quellec1, M Lamard, G Cazuguel

  • 1Institut Telecom, Telecom Bretagne, UEB, Dpt ITI, Brest F-29200, France. gwenole.quellec@telecom-bretagne.eu

Medical Image Analysis
|December 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel content-based image retrieval (CBIR) method for medical diagnosis. The system uses wavelet transforms to create image signatures, enabling efficient retrieval of similar medical images for physician review.

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Last Updated: Jun 17, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

  • Medical Imaging
  • Computer Science
  • Biomedical Engineering

Background:

  • Accurate medical image retrieval is crucial for diagnosis and treatment planning.
  • Existing methods often require domain-specific feature extraction, limiting their applicability.
  • A generic yet adaptable approach is needed for diverse medical imaging applications.

Purpose of the Study:

  • To develop a content-based image retrieval (CBIR) system for aiding medical diagnosis.
  • To create a flexible system adaptable to various pathologies and imaging modalities.
  • To enhance the efficiency and accuracy of medical image retrieval.

Main Methods:

  • Image indexing using generic signatures derived from wavelet transform coefficients.
  • Definition of a distance measure to compare image signatures for similarity assessment.
  • Adaptation of wavelet basis and decomposition schemes using a lifting framework.
  • Optimization of system parameters based on medical grading for performance tuning.

Main Results:

  • The proposed CBIR system demonstrated promising performance across multiple databases.
  • Mean precision achieved was 56.50% for diabetic retinopathy, 70.91% for mammography, and 96.10% for a general database.
  • The system's adaptability allowed for effective retrieval across different medical conditions and image types.

Conclusions:

  • The developed CBIR method offers a flexible and effective approach for medical image retrieval.
  • Wavelet transform-based signatures provide a robust foundation for content-based image analysis in medicine.
  • The system's tunable parameters enable customization for specific diagnostic tasks, improving physician support.