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A hybrid multi-panel image segmentation framework for improved medical image retrieval system.

Faqir Gul1, Mohsin Shah2, Mushtaq Ali1

  • 1Department of Computer Science & IT, Hazara University, Mansehra, Pakistan.

Plos One
|February 20, 2025
PubMed
Summary

This study introduces a hybrid framework to improve medical image retrieval by accurately segmenting multi-panel diagnostic images. The new method enhances sub-image extraction for better medical data consolidation and analysis.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Information Retrieval

Background:

  • Multi-panel images are crucial for medical diagnostics, comprising ~50% of medical literature.
  • These images consolidate diverse patient data (X-rays, MRIs, CT scans) for comprehensive diagnosis.
  • Extracting sub-images from regular/irregular layouts is challenging for current medical image retrieval systems.

Purpose of the Study:

  • To develop a novel hybrid framework for enhanced sub-image retrieval from multi-panel medical images.
  • To address challenges in segmenting both regular and irregular multi-panel medical image layouts.
  • To improve the accuracy and efficiency of medical image retrieval systems.

Main Methods:

  • A hybrid framework combining image classification, computer vision, and image processing techniques.
  • Utilized image projection profiles and morphological operations for precise segmentation.
  • Developed efficient segmentation methods for regular and irregular multi-panel medical images.

Main Results:

  • Achieved 90.50% accuracy in medical image type identification.
  • Attained 91% accuracy in segmenting regular multi-panel images.
  • Achieved 92% accuracy in segmenting irregular multi-panel images.

Conclusions:

  • The proposed hybrid framework significantly enhances sub-image retrieval from diverse multi-panel medical images.
  • Accurate and efficient segmentation across regular and irregular layouts improves medical image retrieval system performance.
  • This approach holds substantial potential for advancing medical diagnostics and literature analysis.