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

An adaptive enhancement and magnetic resonance technology, applied in the field of medical image processing, can solve problems such as inability to effectively suppress image noise, easy loss of useful image information, and sensitive threshold selection

Inactive Publication Date: 2019-02-15
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, there are some problems and deficiencies in the threshold noise suppression method based on wavelet: it is sensitive to the selection of the threshold, if the threshold is too large, the image noise cannot be effectively suppressed, and if the threshold is too small, it is easy to lose useful information in the image
[0005] In order to overcome the problems in the abov...

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

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[0098] The present invention proposes a brain magnetic resonance body data adaptive enhancement method of a transform domain HMT model, and the technical scheme flow chart of the method is as follows figure 1 shown. The specific implementation is described as follows.

[0099] The first step is to collect brain MRI data. The resolution of the collected 3T brain magnetic resonance images is 0.6×0.6×0.6mm 3 . Since the whole brain magnetic resonance image is relatively large, it is not easy to analyze the performance of the enhancement method, so a part of the whole brain magnetic resonance image is intercepted, and its size is 64×64×64.

[0100] The second step is three-dimensional discrete wavelet transform. For brain MRI data, the three-dimensional discrete wavelet transform is realized by discrete wavelet transform along the directions of the three coordinate axes respectively. The number of layers of discrete wavelet transform decomposition is 2 to 5 layers.

[0101] ...

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Abstract

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 adaptive enhancement method has better visual information fidelity than wavelet threshold enhancement method.

Description

technical field [0001] The invention belongs to the field of medical image processing, and relates to a method for adaptive enhancement of brain magnetic resonance volume data, in particular to a method for adaptive enhancement of brain magnetic resonance volume data of a transform domain HMT (Hidden Markov Tree) model. Background technique [0002] Magnetic Resonance Imaging (MRI) is a very important advanced medical imaging technology and a revolutionary medical diagnostic tool. This imaging technology can scan a person in all directions without causing harm to the detected object, and uses the characteristics of different energy levels and phase changes caused by different numbers of hydrogen nuclei in different tissues of the human body to correspond to the movement of atomic nuclei. Construct a high-precision tomographic stereoscopic image containing pathological diagnosis information of human tissue. However, during the imaging process, many factors can lead to image ...

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Application Information

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IPC IPC(8): G06T7/00G06T5/00
CPCG06T5/002G06T7/0012G06T2207/10088G06T2207/20064
Inventor 程和伟覃恒基李章勇王伟赵德春田银冉鹏刘洁
Owner CHONGQING UNIV OF POSTS & TELECOMM
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