Novel denoising technique for optical coherence tomography images

Jarjish Rahaman; Julia May; Carolina Puyana; Maria Tsoukas; Kamran Avanaki

doi:10.1117/12.3003575

12 March 2024 Novel denoising technique for optical coherence tomography images

Jarjish Rahaman, Julia May, Carolina Puyana, Maria Tsoukas, Kamran Avanaki

Proceedings Volume 12816, Photonics in Dermatology and Plastic Surgery 2024; 128160I (2024) https://doi.org/10.1117/12.3003575
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

This study introduces a learning-assisted denoising technique for skin Optical Coherence Tomography (OCT) images. By combining Reinforcement Learning (RL) with the Denoising Convolutional Neural Network (DnCNN), we achieve enhanced denoising capabilities. The method iteratively refines DnCNN parameters through RL-guided policies, demonstrating superior performance. Tailored for skin OCT images, the approach prioritizes preserving vital structures for accurate clinical assessments. This integration of RL into DnCNN training represents a promising advancement in medical image denoising, particularly for dermatological diagnostics.

Citation Download Citation

Jarjish Rahaman, Julia May, Carolina Puyana, Maria Tsoukas, and Kamran Avanaki "Novel denoising technique for optical coherence tomography images", Proc. SPIE 12816, Photonics in Dermatology and Plastic Surgery 2024, 128160I (12 March 2024); https://doi.org/10.1117/12.3003575

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