Denoising is usually one of the major steps in segmentation preprocessing. We investigate noise suppression algorithms known from computer science for their usability for OCT images. Furthermore, new ways of thinking have to be evolved: Speckle noise suppression on some commercially available OCT images is currently performed by averaging multiple frames. We develop methods, that are capable of a better differentiation between image information and speckle noise on multiframe OCT data sets.

We published a denoising algorithm utilizing multiple OCT frames:

• Markus A. Mayer, Anja Borsdorf, Martin Wagner, Joachim Hornegger, Christian Y. Mardin, and Ralf P. Tornow: "Wavelet denoising of multiframe optical coherence tomography data", Biomedical Optics Express, Vol. 3, Issue 3, pp. 572-589 (2012),

  dx.doi.org/10.1364/BOE.3.000572




 

An Matlab implementation as well as the data set used in the evaluation section of the paper can be downloaded from our Image Denoising Algorithms Archives

An early version of a multiframe denoising algorithm based on a wavelet analysis was published in the following form:

 

• Markus A. Mayer, Martin Wagner, Joachim Hornegger, Ralf. P. Tornow: "Wavelet Denoising of Multiple-Frame OCT Data Enhanced by a Correlation Analysis", The Association for Research in Vision and Ophthalmology, Inc. (ARVO) (Annual Meeting) in Fort Lauderdale, Florida, USA, 2010. Poster Download


• Martin Wagner, Anja Borsdorf, Markus A. Mayer, Ralf P. Tornow:

  "Wavelet Based Approach to Multiple-Frame Denoising of OCT-Images",

  Proceedings of the 5th Russian Bavarian Conference on Bio-Medical Engineering (RBC) (5th Russian Bavarian Conference on Bio-Medical Engineering (RBC), München, Deutschland, 2009, pp. 67-69. Paper Download