Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex...

Area of Science:

• Ophthalmology
• Biomedical Imaging
• Signal Processing

Background:

• Image enhancement of retinal structures in optical coherence tomography (OCT) scans is crucial for diagnosing eye diseases.
• Speckle noise in OCT images degrades image quality, hindering accurate diagnosis.

Purpose of the Study:

• To apply a novel locally adaptive denoising algorithm to reduce speckle noise in retinal OCT images.
• To compare the proposed algorithm's performance against the traditional multiple frame averaging technique.

Main Methods:

• A locally adaptive denoising algorithm utilizing a combination of double-density wavelet transform and dual-tree complex wavelet transform was developed.
• The algorithm was applied to reduce speckle noise in retinal OCT images.

Main Results:

• The proposed algorithm achieved comparable image quality to multiple frame averaging but required significantly less acquisition time (an order of magnitude less).
• Improvements in image quality metrics and a 5 dB increase in signal-to-noise ratio were observed.
• The algorithm overcomes limitations associated with eye movements in frame acquisition.

Conclusions:

• The developed denoising algorithm offers an efficient and effective method for enhancing retinal OCT images.
• This technique has the potential to improve the speed and accuracy of diagnosing various eye diseases.