Denoising OCT images using steered mixture of experts with multi-model inference

Aytaç Özkan; Elena Stoykova; Thomas Sikora; Violeta Madjarova

doi:10.1117/12.3000625

12 March 2024 Denoising OCT images using steered mixture of experts with multi-model inference

Aytaç Özkan, Elena Stoykova, Thomas Sikora, Violeta Madjarova

Proceedings Volume 12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; 1283009 (2024) https://doi.org/10.1117/12.3000625
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

In Optical Coherence Tomography (OCT), speckle noise significantly hampers image quality, affecting diagnostic accuracy. Current methods, including traditional filtering and deep learning techniques, have limitations in noise reduction and detail preservation. Addressing these challenges, this study introduces a novel denoising algorithm, Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE). This method combines block-matched implementation of the SMoE algorithm with an enhanced autoencoder architecture, offering efficient speckle noise reduction while retaining critical image details. Our method stands out by providing improved edge definition and reduced processing time. Comparative analysis with existing denoising techniques demonstrates the superior performance of BM-SMoE-AE in maintaining image integrity and enhancing OCT image usability for medical diagnostics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Aytaç Özkan, Elena Stoykova, Thomas Sikora, and Violeta Madjarova "Denoising OCT images using steered mixture of experts with multi-model inference", Proc. SPIE 12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, 1283009 (12 March 2024); https://doi.org/10.1117/12.3000625

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