Three Dimensional Data-Driven MultiScale Atomic
Representation of Optical Coherence Tomography
Abstract— Three Dimensional Data-Driven Multi Scale Atomic Representation of Optical Coherence Tomography. We discuss about applications of different methods for decomposing a signal over elementary wave forms chosen in a family called a dictionary atomic representations< Final Year Projects 2016 > in optical coherence tomography (OCT). If the representation is learned from the data, a nonparametric dictionary is deﬁned with three fundamental properties of being data-driven, applicability on 3D, and working in multi-scale, which make it appropriate for processing of OCT images. We dis cuss about application of such representations including complex wavelet based K-SVD, and diffusion wavelets on OCT data. We introduce complex wavelet based K-SVD to take advantage e of adaptability in dic-tionary learning methods to improve the performance of simple dual tree complex wavelets in speckle reduction of OCT datasets in 2D and 3D. The algorithm is evaluated on 144 randomly selected slices from twelve 3D OCTs taken by Topcon 3D OCT-1000 and Cirrus Zeiss Meditec.
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