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Irina N Dolganova1,2,3, Nikita V Chernomyrdin1,2,4, Polina V Aleksandrova1

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We developed a new nanoparticle-enhanced method to denoise optical coherence tomography (OCT) images using wavelet transforms. This technique significantly improves image quality for biomedical applications.

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Area of Science:

  • Biomedical Imaging
  • Signal Processing
  • Nanotechnology

Background:

  • Optical coherence tomography (OCT) is crucial for high-resolution biological imaging.
  • Image noise in OCT limits diagnostic accuracy and requires effective denoising solutions.
  • Existing denoising methods may not optimally address noise characteristics specific to OCT.

Purpose of the Study:

  • To introduce a novel nanoparticle-enabled, experimentally trained wavelet-domain denoising method for OCT.
  • To develop an adaptive filtering approach for OCT image enhancement.
  • To validate the effectiveness of the proposed denoising technique on biological samples.

Main Methods:

  • Utilized a test object with known properties (nanoparticle suspension with microscale inclusions) for experimental training.
  • Employed a wavelet-domain approach, exploring combinations of wavelet families and filtration thresholds.
  • Developed an experimental training algorithm to construct an optimal wavelet filter based on test object imaging.

Main Results:

  • The proposed method successfully denoises OCT images.
  • Demonstrated high efficiency in improving the quality of in vitro OCT images of human brain meningioma.
  • Identified optimal wavelet decomposition bases and filtration thresholds for OCT denoising.

Conclusions:

  • The nanoparticle-enabled wavelet-domain denoising technique is highly effective for OCT.
  • This method offers a significant advancement in OCT image processing for medical diagnostics.
  • The experimental training approach provides a robust framework for developing tailored denoising filters.