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Interactive radiographic image retrieval system.

Malay Kumar Kundu1, Manish Chowdhury2, Sudeb Das1

  • 1Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India.

Computer Methods and Programs in Biomedicine
|February 12, 2017
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Summary

This study introduces an efficient content-based medical image retrieval system for radiographic images. The novel two-stage approach significantly improves diagnostic accuracy and reduces computational costs, outperforming existing methods.

Keywords:
Content based medical image retrievalFuzzy logicMultiscale geometric analysisPulse couple neural networkRadiographic imagesRelevance feedback

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Artificial Intelligence

Background:

  • Content-based medical image retrieval (CBMIR) systems are crucial for fast diagnosis but often face challenges.
  • Existing CBMIR systems are computationally expensive and struggle with the "semantic gap" and high variability in medical image databases.
  • There is a need for efficient and effective medical image retrieval systems.

Purpose of the Study:

  • To propose a novel, interactive, two-stage CBMIR system for diverse radiographic images.
  • To enhance diagnostic speed and accuracy through quantitative assessment of visual information.
  • To develop a computationally efficient retrieval system that addresses the "semantic gap" problem.

Main Methods:

  • Employs a two-stage retrieval process for radiographic images.
  • Utilizes Pulse Coupled Neural Network (PCNN) for shape features and Non-subsampled Contourlet Transform (NCT) for texture features.
  • Incorporates a fuzzy index-based relevance feedback mechanism to bridge the "semantic gap" and uses Maximal Information Compression Index (MICI) for feature selection.

Main Results:

  • Achieved an overall average precision of approximately 98% on a large dataset of 10,902 radiographic images.
  • Demonstrated high retrieval effectiveness within 2-3 iterations of the relevance feedback mechanism.
  • Outperformed several state-of-the-art CBMIR systems in radiographic image retrieval.

Conclusions:

  • The proposed two-stage hierarchical framework prioritizes efficient feature representation and search-space reduction.
  • Effectively addresses the "semantic gap" without compromising retrieval performance.
  • The system demonstrates efficient performance in radiographic medical image retrieval.