Image enhancement of retinal structures, in optical coherence tomography (OCT) scans through denoising, has the potential to aid in the diagnosis of several eye diseases. In this paper, a locally adaptive denoising algorithm using double-density dual-tree complex wavelet transform, a combination of the double-density wavelet transform and the dual-tree complex wavelet transform, is applied to reduce speckle noise in OCT images of the retina. The algorithm overcomes the limitations of commonly used multiple frame averaging technique, namely the limited number of frames that can be recorded due to eye movements, by providing a comparable image quality in significantly less acquisition time equal to an order of magnitude less time compared to the averaging method. In addition, improvements of image quality metrics and 5 dB increase in the signal-to-noise ratio are attained.
Bibliographical noteFunding Information:
The authors thank Dr. Ivan Selesnick of Polytechnic University, Brooklyn, NY for giving us advice to perform the software and providing Double-Density Wavelet Software. This research was supported by Seymour Fisher Scholarship from Ophthalmology Department at UTMB Health and funding for Research to Prevent Blindness (RPB) to UTMB Health and the University of Minnesota. The authors gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German Research Foundation (DFG) in the framework of the German excellence initiative.
- image enhancement
- optical coherence tomography
- wavelet transforms