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Medical Image Denoising Using Hierarchical Hidden Markov Model in the Wavelet Domain

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3 Author(s)
Jixiang Zhang ; Electron. Eng. Dept., Tianjin Univ. of Technol. & Educ., Tianjin ; Xiangling Zhang ; Zhijun Pei

Wavelet-domain hidden Markov models (HMMs) have been recently proposed and applied to image processing, e.g., image denoising. In this paper, we develop a new HMM, called hierarchical hidden Markov tree model (HHMT), by adopting a feasible and fast two stage algorithm which avoids the time-consuming training process to estimate the HMT model parameters. The HHMT can exploit both the local statistics and the interscale dependencies of wavelet coefficients at a low computational complexity. We show that the HHMT model can achieve state-of-the-art medical image denoising performance.

Published in:

Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on  (Volume:2 )

Date of Conference:

7-8 March 2009