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Related Experiment Video

Updated: Jan 27, 2026

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Smooth Gyrated Texel Quadrivium Network for 3D Retinal OCT Image Compression.

N Nanthini1, S Sasipriya2, M Ramkumar3

  • 1Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India.

Journal of Clinical Ultrasound : JCU
|January 26, 2026
PubMed
Summary

A new Smooth Gyrated Texel Quadrivium Network (SGTQN) effectively denoises retinal optical coherence tomography images by addressing signal-dependent and hybridized low-contrast residual noise (HLRN). This method enhances image compression and segmentation quality.

Keywords:
Ascombe transform (AT)Texel Quadrivium networkhybridized noise evictionlow‐contrast residual noisenon‐diagnostic areastandardized gyrated layer

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Retinal optical coherence tomography (OCT) image compression requires effective pre-processing for noise reduction.
  • Existing denoising methods struggle with signal-dependent noise and hybridized low-contrast residual noise (HLRN).
  • Current segmentation techniques often overlook retinal nerve deviation, impacting image interpretability.

Purpose of the Study:

  • To propose a novel network, the Smooth Gyrated Texel Quadrivium Network (SGTQN), for advanced OCT image denoising and information gathering.
  • To develop an improved segmentation method that incorporates retinal nerve deviation for self-explanatory images.
  • To enhance OCT image compression quality through uniform compression techniques.

Main Methods:

  • The SGTQN utilizes an Additive Ascombe Smooth Sifter to convert Poisson noise to Gaussian noise and remove HLRN.
  • An Improvised Gyrated Alexa Net with a Standardized Gyrated Layer is employed for segmentation, considering deviation values.
  • A Texel Quadrivium Convolutional Network modifies the pooling layer to a Texel Quadrivium Layer for uniform compression.

Main Results:

  • The proposed model effectively reduces both signal-dependent and HLRN in OCT images.
  • The segmentation method generates self-explanatory images by incorporating retinal nerve deviation.
  • High-quality image compression is achieved with uniform compression and adjuvant vector coordinates.
  • The model demonstrates a high accuracy of 95% and a low mean square error of 0.02.

Conclusions:

  • The SGTQN offers a robust solution for denoising and enhancing information extraction in retinal OCT images.
  • The integrated segmentation approach improves image interpretability by considering nerve deviation.
  • The novel compression technique yields high-resolution, high-quality compressed images, advancing OCT analysis.