Speckle noise reduction algorithm with total variation regularization in optical coherence tomography.

Area of Science:

• Medical Imaging
• Image Processing
• Biomedical Engineering

Background:

• Optical Coherence Tomography (OCT) is crucial in medical imaging.
• OCT images are degraded by inherent speckle noise, affecting diagnostic accuracy.
• Existing despeckling methods often struggle with preserving fine details and edges.

Purpose of the Study:

• To develop and evaluate a novel speckle noise reduction algorithm for OCT images.
• To enhance image quality by effectively removing speckle while preserving critical image features.
• To assess the algorithm's performance against established methods using various quantitative metrics.

Main Methods:

• A speckle noise reduction algorithm employing total variation (TV) regularization was developed.
• The algorithm incorporates a constructed regularization parameter and a tuning function for optimized performance.
• Performance was evaluated through simulations, comparing visual quality, processing time, and metrics like SNR, ENL, CNR, RMSE, and edge preservation.

Main Results:

• The proposed TV regularization algorithm significantly reduced speckle noise in OCT images.
• It demonstrated superior edge preservation compared to classical and typical despeckling algorithms.
• Quantitative analysis showed improvements in SNR, ENL, and CNR, with reduced recovery error and efficient processing time.

Conclusions:

• The developed algorithm offers effective speckle noise reduction and excellent edge preservation for OCT images.
• Its performance surpasses existing methods in key image quality metrics and efficiency.
• The algorithm shows promise for in-device OCT preprocessing, advancing clinical diagnosis and analysis.