Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary

Raheleh Kafieh; Hossein Rabbani

doi:10.1117/12.2026520

26 September 2013 Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary

Raheleh Kafieh, Hossein Rabbani

Author Affiliations +

Proceedings Volume 8858, Wavelets and Sparsity XV; 885826 (2013) https://doi.org/10.1117/12.2026520
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

Optical Coherence Tomography (OCT) suffers from speckle noise which causes erroneous interpretation. OCT denoising methods may be studied in "raw image domain" and "sparse representation". Comparison of mentioned denoising strategies in magnitude domain shows that wavelet-thresholding methods had the highest ability, among which wavelets with shift invariant property yielded better results. We chose dictionary learning to improve the performance of available wavelet-thresholding by tailoring adjusted dictionaries instead of using pre-defined bases. Furthermore, in order to take advantage of shift invariant wavelets, we introduce a new scheme in dictionary learning which starts from a dual tree complex wavelet. we investigate the performance of different speckle reduction methods: 2D Conventional Dictionary Learning (2D CDL), Real part of 2D Dictionary Learning with start dictionary of dual-tree Complex Wavelet Transform (2D RCWDL) and Imaginary part of 2D Dictionary Learning with start dictionary of dual-tree Complex Wavelet Transform (2D ICWDL). It can be seen that the performance of the proposed method in 2D R/I CWDL are considerably better than other methods in CNR.

Citation Download Citation

Raheleh Kafieh and Hossein Rabbani "Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary", Proc. SPIE 8858, Wavelets and Sparsity XV, 885826 (26 September 2013); https://doi.org/10.1117/12.2026520

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available