Speckle reduction of OCT images using an adaptive cluster-based filtering

Saba Adabi; Elaheh Rashedi; Silvia Conforto; Darius Mehregan; Qiuyun Xu; Mohammadreza Nasiriavanaki

doi:10.1117/12.2254903

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17 February 2017 Speckle reduction of OCT images using an adaptive cluster-based filtering

Saba Adabi, Elaheh Rashedi, Silvia Conforto, Darius Mehregan, Qiuyun Xu, Mohammadreza Nasiriavanaki

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Proceedings Volume 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI; 100532X (2017) https://doi.org/10.1117/12.2254903
Event: SPIE BiOS, 2017, San Francisco, California, United States

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 grainy pattern, called speckle, due to the broadband source that has been used 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 a method for speckle reduction of OCT skin images. Considering the architectural structure of skin layers, it seems that a skin image can benefit from being segmented in to differentiable clusters, and being filtered separately in each cluster by using a clustering method and filtering methods such as Wiener. The proposed algorithm was tested on an optical solid phantom with predetermined optical properties. The algorithm was also tested on healthy skin images. The results show that the cluster-based filtering method can reduce the speckle and increase the signal-to-noise ratio and contrast while preserving the edges in the image.

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Saba Adabi, Elaheh Rashedi, Silvia Conforto, Darius Mehregan, Qiuyun Xu, and Mohammadreza Nasiriavanaki "Speckle reduction of OCT images using an adaptive cluster-based filtering ", Proc. SPIE 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, 100532X (17 February 2017); https://doi.org/10.1117/12.2254903

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