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Journal of Applied Sciences ›› 2011, Vol. 29 ›› Issue (5): 467-472.doi: 10.3969/j.issn.0255-8297.2011.05.005

• Signal and Information Processing • Previous Articles     Next Articles

Denoising of Optical Coherence Tomography Image Using Dual-Tree Complex Wavelet Transform and Mixed Probability Model

SHU Peng1, SUN Yan-kui1, TIAN Xiao-lin2   

  1. 1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
    2. Faculty of Information Technology, Macau University of Science and Technology, Macao SAR 999078, China
  • Received:2010-09-06 Revised:2011-07-08 Online:2011-09-28 Published:2011-09-30

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Abstract:

 To remove speckle noise in optical coherence tomography (OCT) images, the ProbShrink algorithm based on the dual-tree complex wavelet transform and a mixed probability model is employed. After studying the signal and noise distribution in OCT images, a mixed probability model in microscopic-level is introduced.
Logarithm of the OCT image is first decomposed using dual-tree complex wavelet transform. The coefficients consistent with edges obey the generalized Gaussian distribution, while others obey the Gaussian distribution. An improved ProbShrink algorithm is used to shrink the wavelet coefficients. Experiments show that this
method can significantly improve signal-to-noise ratio while hold edge preservation index relatively steady. The performance is better than that of traditional wavelet based OCT image denoising methods.

Key words: optical coherence tomography, image denoising, dual-tree complex wavelet transform, mixed probability model, ProbShrink algorithm

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