An Adaptive Cluster-based Wiener Filter for Speckle Reduction of OCT Skin Images
Implemented by: Elaheh Rashedi, Saba Adabi
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Abstract
Abstract: Optical coherence tomography (OCT) has become a favorable device in the dermatology discipline due to its moderate resolution and penetration depth. OCT images however contain a grainy pattern, called speckle, due to the use of a broadband source in the configuration of OCT. So far, a variety of filtering techniques is introduced to reduce speckle in OCT images. Most of these methods are generic and can be applied to OCT images of different tissues. In this paper, we present an adaptive filtering method, optimized for speckle reduction of OCT skin images. Considering the architectural structure of skin layers, OCT skin images can be segmented into differentiable clusters. The image in each cluster is then filtered by a Wiener filter. The proposed method was tested on optical solid phantoms with predetermined optical properties. The algorithm was also tested on healthy human skin images. The results show that the proposed cluster-based filtering method can effectively reduce the speckle and increase the signal-to-noise ratio and contrast while preserving the edges in the image.
Keywords: optical coherence tomography, skin images, speckle reduction, Wiener filtering, clustering.