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IntOPMICM: Intelligent Medical Image Size Reduction Model.

Piyush Kumar Pareek1, Chethana Sridhar2, R Kalidoss3

  • 1Department of Computer Science Engineering & Head IPR Cell, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India.

Journal of Healthcare Engineering
|March 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces IntOPMICM, a novel medical image compression technique combining genetic algorithms and vector quantization. It achieves superior image quality and compression ratios compared to existing methods.

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Area of Science:

  • Medical Imaging
  • Image Compression
  • Computational Intelligence

Background:

  • Increasing use of medical images necessitates efficient compression.
  • Lossy compression methods face challenges in balancing compression ratio and image quality.
  • Existing techniques often struggle to maintain diagnostic accuracy.

Purpose of the Study:

  • To introduce the IntOPMICM technique for enhanced medical image compression.
  • To evaluate the performance of IntOPMICM against existing methods.
  • To improve the compression ratio and image quality metrics for medical imaging.

Main Methods:

  • Developed IntOPMICM, a hybrid scheme combining Genetic Particle Swarm Optimization (GenPSO) and Vector Quantization (VQ).
  • Utilized a codebook generated through a combination of fragments and genetic algorithms.
  • Evaluated performance using metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Normalized Mean Squared Error (NMSE), Signal-to-Noise Ratio (SNR), and Compression Ratio (CR).

Main Results:

  • IntOPMICM demonstrated higher PSNR and SSIM values at given compression ratios compared to existing methods.
  • The technique achieved lower MSE, Root Mean Squared Error (RMSE), and SNR values for comparable compression ratios.
  • Experimental results on real-time medical imaging validated the proposed approach's effectiveness.

Conclusions:

  • The IntOPMICM technique offers a significant improvement in medical image compression.
  • It provides a better balance between compression efficiency and diagnostic image quality.
  • IntOPMICM represents a promising advancement for medical imaging applications.