Truncated Dirichlet process infinite Student'st' hybrid model-based brain nuclear magnetic resonance image segmentation method

Abstract

The invention relates to a truncated Dirichlet process infinite Student'st' hybrid model-based brain nuclear magnetic resonance image segmentation method. The method includes the following steps that: based on a Dirichlet process infinite Student'st' hybrid model, a component number in the infinite Student'st' hybrid model is assumed to be a preset image segmentation number K; an expectation maximization algorithm is adopted to solve the model; and image segmentation is carried out by using a Bayesian maximum posterior probability criterion. According to the method of the invention, the assumed Student'st' hybrid model directly corresponds to different portions of a brain nuclear magnetic resonance image; the high tail characteristics of Student'st' distribution determines that the model has a good anti-noise effect, so that the segmentation of the brain nuclear magnetic resonance image can be realized; in the processing of solving the Dirichlet process infinite Student'st' hybrid model, the simple and efficient expectation maximization algorithm is adopted to solve the Dirichlet process infinite Student'st' hybrid model, and the solving of the model is easier to realized; and one brain nuclear magnetic resonance image can be quickly and automatically segmented at a PC end.

Description

technical field [0001] The present invention relates to computer-aided image processing technology, more specifically, relates to a kind of brain MRI image segmentation method based on truncated Dirichlet process infinite Student's t mixture model. Background technique [0002] In recent decades, medical imaging technology has developed rapidly. In particular, magnetic resonance imaging technology has the advantages of non-invasiveness, inspection range covering various systems of the human body, and rich imaging data. It is the most widely used. Medical image analysis methods have been concerned, researched and applied. Medical image segmentation is an important research content in medical image analysis. [0003] Medical image segmentation is to divide the image into several regions according to the similarity in the medical image region and the different characteristics between regions, mainly including studying anatomical structures, identifying regions of interest, obse...

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Problems solved by technology

[0009] Literature (Sfikas G, Nikou C, Galatsanos N.Robust image segmentation with mixtures of student's t-distributions[C] / / Image Processing, 2007.ICIP2007.IEEE International Conference on.IEEE,2007,1:I-273-I-276 .) Use the EM algorithm to solve the finite Student's t mixture model for brain MRI image segmentation. However, the obvious disadvantage of this method is that it cannot automatically determine the number of segments

[0010] Literature (da Silva A R F.A Dirichlet process mixture model for brain MRItissue classification[J].Medical Image Analysis,2007,11(2):169-182.) using the non-parametric Bayesian model of the Dirichlet process to achieve brain MRI Resonance image segmentation, but he uses complex sampling calculations, and he assumes that the data obeys a Gaussian distribution, which is sensitive to noise in brain MRI images

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