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Related Experiment Video

Updated: Oct 9, 2025

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging
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Objective Numerical Evaluation of Diffuse, Optically Reconstructed Images Using Structural Similarity Index.

Vicky Mudeng1,2, Minseok Kim3,4, Se-Woon Choe1,5

  • 1Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea.

Biosensors
|December 23, 2021
PubMed
Summary

This study introduces a new algorithm for objective assessment of diffuse optical tomography images. The multiscale, improved structural similarity (SSIM) algorithm (MS-ISSIM-S) shows promise for accurately evaluating tissue inclusions.

Keywords:
biosensorsdiffuse optical tomographyhuman visible perceptionnumerical evaluationstructural similarity

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Image Processing

Background:

  • Diffuse optical tomography (DOT) is a non-invasive imaging technique.
  • DOT provides tissue optical properties distribution for structural information.
  • Current assessment of DOT images relies on subjective measurements.

Purpose of the Study:

  • To develop a numerical algorithm for objective assessment of DOT reconstructed images.
  • To evaluate the performance of different structural similarity (SSIM) algorithms for identifying inclusions in DOT images.
  • To propose a novel SSIM-based metric that aligns with human visual perception.

Main Methods:

  • Compared four SSIM algorithms using 168 simulated DOT reconstructed images.
  • Varied inclusion contrast ratios and sizes in simulated images.
  • Proposed and evaluated a multiscale, improved SSIM with a sharpness parameter (MS-ISSIM-S).

Main Results:

  • The proposed MS-ISSIM-S metric demonstrated suitability for human visual perception.
  • MS-ISSIM-S showed reduced similarity scores with varying contrasts for similar inclusion sizes.
  • The algorithm effectively assesses reconstructed images for identifying inclusions.

Conclusions:

  • The MS-ISSIM-S metric offers a promising objective numerical assessment for DOT images.
  • This method can improve the reliability and consistency of DOT image evaluation.
  • Objective assessment using MS-ISSIM-S aids in locating tissue inclusions.