Study On Brain MR Images And Chinese Visual Human Brain Images Segmentation

The application of magnetic resonance imaging, with the characteristics of no intervention,not harmful,seldom effected by the motions of objection, has been used in taking pictures of medical images. Medical image segmentation plays an important role in biomedical research and clinical applications such as study of anatomical structure, quantification of tissue volumes, localization of pathology, diagnosis, treatment planning, and computer aided surgery, etc. As a result, accurate segmentation method is crucial to the follow-up analysis.According to different image analysis task, medical image segmentation aims at partition the original image into several meaningful regions or isolating the region of interesting (ROI). Variational method could naturally convert complex segmentation into a variational functional optimization problem. In this thesis, variarional method-based medical image segmentation for specific tasks is extensively explored, and efficient numerical algorithms are discussed.The application of magnetic resonance imaging, with the characteristics of no intervention,not harmful,seldom effected by the motions of objection, has been used in taking pictures of medical images. Chinese Visible Human images can give the true information about human tissues. These images segmentation plays an important role in biomedical research and clinical applications such as study of anatomical structure, quantification of tissue volumes, localization of pathology, diagnosis, treatment planning, and computer aided surgery, etc. As a result, accurate segmentation method is crucial to the follow-up analysis.According to different image analysis task, medical image segmentation aims at partition the original image into several meaningful regions or isolating the region of interesting (ROI). Variational method could naturally convert complex segmentation into a variational functional optimization problem. In this thesis, variarional method-based medical image segmentation for specific tasks is extensively explored, and efficient numerical algorithms are discussed.The segmentation of the brain images can be separated into a few steps: 1) image denoising. Because of the effect of ficilities, the images have noise in images. Denoise the image can make the segmentation more exactly; 2) Skull stripping. In the brain images the skull and other tissues take a large part, so a exact skull stripping method can make the segmentation more exact; 3) de-bias. The bias field in images can make images inhomogeneous and hard to segment; 4) segment the images. Use methods such as active counter models to find region of interesting. This paper wants to form a union image segmentation framework, which has a high intelligent ability in order to solve some complex image segmentation problems. The primary work and remarks of this paper are as follows:(1) A new anisotropic diffusion based image denoising method is proposed by analyzing the traditional important denoising models: harmonical model, CTV model. At first, three requirements of image denoising are proposed. Using the structure information, the new model can anisotropic diffuse the image in the edge region and isotropic diffuse the flat region. In order to contain the corn region, the corn information is added to the new model. Experimental results show that the new denoising method is capable of sufficiently preserving geometric information such as edges and corners in addition to its effectiveness for image denoising(2) Traditional Level Set method can not get prefer results for it only depend on the gradient information. In this paper a new speed function is presented which is based on anisotropic diffusion function, Gaussian mixture model and global information. With the new speed function the adapted level set model can get better results. The experiments to skull stripping the brain MR images show that the method of this paper can get better results in an accuracy way.(3) We propose a new de-bias model based on entropy method. The best bias will make the image has the smallest entropy, but it need to find a lot of parameter to compute the bias. The traditional method uses the gradient descending method to find the parameters. The method plunges in local best easily. In order to deal with this problem, genetics algorithm (GA) method, Particle swarm optimization (PSO) method are analyzed and an adapt method is present to get the global best result. The experiments show that the new method can get accurate result robustly.(4) This paper presents three brain MR images segmentation models:â—†Gaussian mixture model can approach the probability of the image's histogram. Active contour models can be improved with the Gaussian mixture model to be more fit with the segmentation of medical images. But the estimation of the parameters of the model is hard and usually based on the Expection-Maximization (EM) method. Btu the method is local best and sensitivity to its initial parameters. In order to get better results, we use PSO model to compute the parameters. With this parameter, we construct a new constrain force and with this new force the model can get better results. After some experiments, we found that the Gaussian mixture model only uses the information of the histogram and not uses the information of the location of the pixel. So it is sensitive to the noise. In this paper, we give a method to make a new information field, which combines the information of the region, texture and region simulation. With the new information field the Gaussian mixture model can reduce the effect of the noise. In this paper the Gaussian mixture model be introduced to the Level set model and reduce the effect of the noise and prevent the curve over the weak edges. After get the inner edge of the left ventricle, this paper uses the region and shape information to segment the out edge. Experiments on the segmentation of brain magnetic resonance images show this model has better effect in image segmentation.â—†In order to overcome the limitation of Gauss mixture model, this article uses the Gibbs theory and the image structure information to construct anisotropic Gibbs random field and to incorporate it into the Gauss mixture model. The new Gauss mixture model can reduce the effect of the noise and contain the information of beam structure regions and corner regions. Experiments on the segmentation of brain magnetic resonance images show this model can attain better effect in image segmentation.â—†We introduce a new coupled variational model, which can registration and segmentation simultaneous. In the model a couple function is constructed to fuse the non-rigid registration information and the active contour model, which based on the region information. Using this information an energy function is constructed. Through finding the extremum of the energy function the model can realize registration and segmentation simultaneously. The model can be applied to analyze the images which from different modals. The results of experiments show that the model can get better results robustly.(5) This paper presents three Chinese Visible Humane brain images segmentation models:â—†We analyze the images in HSV color space, which can distinguish different areas in brain clearly. In this paper a new fuzzy anisotropic diffusion functions is presented, which can diffuse the images meanwhile preserve the curves. During eliminating other apparatus from brain, the saturation information, hue information, value information, anatomy information, and region information are fused to confirm the results correctly. The experiments to segment the digital human brain images show that the method of this paper can get well results in an accuracy way.â—†C-V model is one of the best segment method, but the classical C-V model only segment the image into object and background; only use the intensity information when segment color images; must re-initial the distance function during evolve the curves. In the CVH images, there are many fake grey matters and with the effect of these fake matters the C-V model can hardly separate grey matters with fake grey matters. To deal with the problem the C-V model is adapted to be able to segment more objects; PCA model is presented to large the difference of grey matters and fake grey matters. With the effect of tissues themselves, there are many in-homogenous phenomenons in the CVH images; the local information is added to model to reduce these effects. Use the distance resistance energy, the model can evolve curves without re-initialization. The Chinese visual human brain images segmentation experimental results show that the method of this paper can get right results in an accuracy way.â—†In order to overcome the limitation of the Mean Shift method, this paper presents a new anisotropic Gauss kernel, which based on structure information, and with the new Gauss kernel the new model can reduce the effect of gracile topological structure. This paper projects the color space to a new space, based on PCA model, to expand the distance of similar color and make more difference between grey matters and grey matters belonging to next picture. The results of the segmentation of the digital brain image show that adapt Mean Shift method can get better results.