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Image Decomposition Technique Based on Near-Infrared Transmission.

Toto AminotoPurnomo Sidi PriambodoHarry Sudibyo
Published in: Journal of imaging (2022)
One way to diagnose a disease is to examine pictures of tissue thought to be affected by the disease. Near-infrared properties are subdivided into nonionizing, noninvasive, and nonradiative properties. Near-infrared also has selectivity properties for the objects it passes through. With this selectivity, the resulting attenuation coefficient value will differ depending on the type of material or wavelength. By measuring the output and input intensity values, as well as the attenuation coefficient, the thickness of a material can be measured. The thickness value can then be used to display a reconstructed image. In this study, the object studied was a phantom consisting of silicon rubber, margarine, and gelatin. The results showed that margarine materials could be decomposed from other ingredients with a wavelength of 980 nm.
Keyphrases
  • deep learning
  • optical coherence tomography
  • diffusion weighted imaging
  • high intensity
  • machine learning
  • hyaluronic acid