  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
(de-speckling) 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. The ambition of this work is to provide a de-speckling framework
specialized for filtering skin tissues for the community to utilize, adapt or
build upon. In this paper, we present an adaptive cluster-based filtering
framework, optimized for speckle reduction of OCT skin images. In this
framework, by considering the layered structure of skin, first the OCT skin
images are segmented into differentiable layers utilizing clustering
algorithms, and then each cluster is de-speckled individually using adaptive
filtering techniques. In this study, hierarchical clustering algorithm and
adaptive Wiener filtering technique are utilized to develop the framework. The
proposed method is tested on optical solid phantoms with predetermined optical
properties. The method is 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. The proposed cluster-based filtering
framework enables researchers to develop unsupervised learning solutions for
de-speckling OCT skin images using adaptive filtering methods, or extend the
framework to new applications.
