An adaptive enhancement method of brain magnetic resonance volumetric data based on transformed domain HMT model

• Main Authors: WANG WEI, QIN HENGJI, RAN PENG, LI ZHANGYONG, TIAN YIN, LIU JIE, CHENG HEWEI, ZHAO DECHUN
• Format: Patent
• Language: Chinese; English
• Published: 15.02.2019
• Subjects: CALCULATING; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

The invention relates to an adaptive enhancement method of brain magnetic resonance volumetric data based on transformed domain HMT model , belonging to the field of medical image processing. The method organically combines wavelet transform and hidden Markov chain. According to the characteristics of non-Gaussian distribution with long tail and high peak value of the probability density functionof single wavelet coefficients, a Gaussian mixture model is established for the randomness of single wavelet coefficients. At the same time, the persistence of wavelet coefficients is described by Hidden Markov Tree (HMT). The wavelet domain Hidden Markov Tree model is established and the EM algorithm is used to solve the model. Using the solution of HMT model, the expectation of wavelet coefficients is estimated in the absence of noise. The inverse wavelet transform of the noise suppressed wavelet coefficients is used to obtain the enhanced MRI volume data. Through subjective and objective evaluation, the wavelet ada