Research On Methods Of Medical Image Enhancement And Segmentation Using PDE

The rapid development of modern imaging techniques improves the resolution of the target, but leads to a significant increase in the amount of data and complexity simultaneously, which puts forward higher requirements on the performance and automaticity of image processing algorithms. As the basic steps to medical image analysis, image enhancement and segmentation are the necessary parts of lesion localization and quantitative assessment thus play an indispensable role in the computer-aided diagnosis and treatment system.For the solid mathematical foundation and the flexible open framework, Partial Differential Equations (PDE) is conducive to integrating other theories and developing new models to meet the needs of different imaging modes and image analysis in different application background, which has become one of the most popular methods in the field of medical image processing. As a footstone of PDE image analysis theory, nonlinear diffusion, the active contour model and level set method have been widely used in image filtering and segmentation in medical image enhancement.This dissertation concentrates on some difficulties in medical image enhancement and segmentation to carry out PDE research on medical image processing and lay the basis of the core technology for the development of computer-aided diagnosis system. The main work and innovations of this dissertation are listed as follows:1) In the area of speckle noise suppression for ultrasound image, a new speckle reducing anisotropic diffuse (NSRAD) is proposed by introducing a sigmoid function to the diffusion coefficient in the traditional SRAD. The basic route of NSRAD is to combine the image characteristics of different regions using segmented diffusion coefficient, in which the diffusion coefficient approaches to a constant number one in the homogeneous regions, and declines rapidly in the transition regions, then approaches to zero in the strong boundary regions. Benefited by the sigmoid function in the new diffusion function, the NSRAD has better noise-suppressing ability, as well as the abilities of retaining details and weak edges, even sharp the strong boundary, compared to the traditional SRAD. Moreover, the control ratio of the homogeneous regions and the speed control coefficient are adjustable, which well meet different application requirements by solving the problem of different homogeneous regions scales in the ultrasound image.2) The traditional multi-phase Chan-Vese model may generate error results as it solely relies on the speed function constructed by the regional mean gray, which may easily cause wave front disorder and lead several evolution curves attracting to the same goal. To solve this problem, a new hierarchical splitting Chan-Vese active contour method for image segmentation is presented. Its basic route is to add the hierarchical splitting segmentation to the basic Chan-Vese model. In detail, the segmentation results are obtained and the sub-images are marked by the upper level segmentation, and then further segmented by extracting the sub-images that need further segmentation. The whole process is terminated when the segmentation results meet different levels of application requirements. The model has all the advantages of the original Chan-Vese model, as well as the following advantages:Firstly, the lower level segmentation is only performed on the sub-image obtained by the upper level segmentation rather than the entire image area. This solves the problem that the segmentation results are incorrect due to the lack of mutual constraints in the multi-phase Chan-Vese model when several curves evolution attract to the same goal. Secondly, only one level set function is considered at each split computing. Accordingly, the mode is easier to achieve compared to the multiphase level set method. Thirdly, segmentation can been deepen gradually to meet different levels of application requirements by the hierarchical method.3) Fuzzy classification of segmentation is a low-level image processing techniques and performs weakly in the automated image processing due to its requirement of subsequent manual boundary connections. However, the active contour is a closed smooth evolution curve, which can fuse the upper prior knowledge. Thus, in this dissertation, a new model is presented to integrate the fuzzy clustering and active contour for image segmentation, especially for medical image segmentation. The basic idea is as follows:by coupling the region information and the boundary one which are calculated by the priori statistical information gotten from the fuzzy clustering, and adding an artificial force not only to increase the robustness and the convergence rate by imposing the idea of mutually exclusive propagating curves but also to constrain regions not overlap and no pixels not assigned to any region, the minimum objective function coupling region and boundary one is established. The PDE corresponding to the defined objective function minimization is established by a gradient descent method. And a level set approach is used to solve the PDE system. The new model has the following advantages:firstly, the new model solves the problem that the boundary requires subsequent connections in the traditional fuzzy classification segmentation for its discontinuity boundary. Secondly, the new model has the ability to freely deal with the topological deformation due to the level set method. Thirdly, the new model is the curves evolution driven by the minimum energy, which can reduce the pseudo-region caused by the isolated noise points. Fourthly, the model can be used not only for image segmentation but also for image tracking.4) Based on the analysis of vascular image differential detection principle, an improved GVF diffusion3D blood vessel image enhancement filter is presented, which fuses maximum flux method into the level set framework for vascular image segmentation. The new blood vessels likelihood function is derived and defined in the new method. It has the following advantages:Firstly, the new blood vessels likelihood function is insensitive to the changes in the axial density by abandoning the eigenvalues for the blood vessels radial direction, which well preserves the axial density. Secondly, the new blood vessels likelihood function can inhibit the overlapped and vague border, and sharpen the edge by adding the inhibitory factor. Thirdly, the new blood vessels likelihood function does not require any parameter, which is conducive to simplify implementation. Moreover, the new method calculated by non-normalized gradient vector field is better than the existing method calculated by the corresponding normalized vector field. The new method can achieve better effect in the image enhance filter, which can well keep the whole topology of the3D blood vessel network, and overcome the deficiency in estimating the blood vessel radius than the existing one.This dissertation contributes to the application domains of PDE method in image processing and provides new techniques for nonlinear diffusion, curve evolution method and the intractable3D blood vessel enhancement filtering and segmentation. The new models and methods proposed in this dissertation widen the research horizon of contemporary and have a promising future.