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RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion.

Metwally Rashad1,2, Ibrahem Afifi3, Mohammed Abdelfatah3

  • 1Department of Computer Science, Faculty of Computers & Artificial Intelligence, Benha University, Benha, Egypt. metwally.rashad@fci.bu.edu.eg.

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|January 26, 2023
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Summary

A new method, RbQE, improves content-based medical image retrieval (CBMIR) for CT and MR scans. It uses feature expansion and deep learning models for more accurate disease understanding and diagnosis.

Keywords:
Deep featuresMean valueMedical image retrievalPre-trained learning modelsQuery expanded

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • The increasing volume of medical images necessitates efficient retrieval systems beyond diagnostics.
  • Content-based medical image retrieval (CBMIR) is crucial for understanding disease causes and treatments.

Purpose of the Study:

  • To propose an efficient and accurate CBMIR method for computed tomography (CT) and magnetic resonance (MR) images.
  • To enhance the understanding of diseases and improve diagnostic capabilities through advanced image retrieval.

Main Methods:

  • The proposed RbQE method expands query features using pre-trained AlexNet and VGG-19 models for deep feature extraction.
  • RbQE employs a two-stage search: a rapid search for query reformulation and a final search for database retrieval.

Main Results:

  • The RbQE method demonstrated superior performance across four public databases (TCIA-CT, EXACT09-CT, NEMA-CT, OASIS-MRI).
  • Average retrieval precision (ARP) improvements ranged from 0.84% to 14.34% compared to state-of-the-art methods.

Conclusions:

  • The RbQE method offers an efficient and accurate solution for CBMIR of CT and MR images.
  • This advancement supports deeper insights into diseases and enhances medical image analysis.