Research Of Nasopharyngeal Carcinoma Cell Image Classification Based On Synergetic Pattern Recognition Method

The automatic recognition and analysis of medicine microscopic image is researched hotspots in biomedical engineering domain. Extraction process of nasopharyngeal carcinoma cell images'corporeal components and key technology of synergetic pattern recognition method are researched in this paper. In the field of extration process to cell images'formed element, intelligent extraction key technology for corporeal components in cell image was researched in this dissertation, which mainly involves cell image filtering, image segmentation, cell edge extraction, overlapped cells segmentation and inpainting technology of overlapped region. Synergetic pattern recognition key technology for nasopharyngeal carcinoma cell images was also rearched in this dissertation, which mainly involves synergetic pattern recognition learning method, synergetic neural network optimizing method and synergetic pattern recognition invariant property.Image filtering includes two ways such as filtering noise and enhancing the edge.The dissertation firstly discussed the factors of lowering quality and analyzed the noise model. The anisotropic diffusion equation self-adapting filter algorithm based on the gradient is proposed. The image background noise and the pulse noise have been seperately filtered by the ameliorative anisotropy fliter and the adaptive median filter, at the same time the detail information were reserved. Secondly, fuzzy clustering method (FCM) optimized by particle swarm optimization (PSO) and coupled with markov random field is discussed, which taking the clustering result of PSO as the initialized value of the FCM. By adopting the couple method of Markov random field and fuzzy clustering to calculate the fitness function, and apply the algorithm to the representative image segmentation to get the center of clustering. Both image guidance information and spatial information imposed by Gibbs smoothness prior to the pixel labels is used to effectively in segmenting the cell images. A multi-scale morphological edge detection algorithm based on evidence syncretic fusion is proposed. Edges of different size were detected by using different scale operator, and cell edge images were combined with the way of evidence syncretic fusion. To the overlapping cells segmentation often appears in the medical microscopic images. A segmentation algorithm for overlapped cell images based on cell scatter plot and modified Snake was proposed. In order to get the mask image based on the overlapped region, a fast image inpainting algorithm was presented based on adaptive iterative convolution to solve the information deterioration resulted from overlapping.Four improved algorithm was presented on synergetic pattern recognition form different point of view. Firstly, A synergetic prototype modify method with particle swarm optimization algorithm is applied to avoided information saturation. The method could get the optimal prototype by the global optimize ability of particle swarm optimization. Secondly, a synergetic training algorithm based on potential energy function optimized is applied form the view of meanwhile learning. The studying of potential energy function dynamics process can train prototype vector and adjoint vector meanwhile. The nonlinearing optimization approach is introduced to synergetic dynamics evolution process, using the memory gradient algorithm instead of the steepest gradient algorithm to optimize the potential energy function. Thirdly, a synergetic classification algorithm based on prototype vectors fusion with sparse decomposition is applied from the view of reduction experiment mode relativity and redundant information. It's a trend of the recognition way research in synergetic vectors that the character value is used as prototype vectors instead of image pixel. Contourlet transform is new image representation scheme which have directionality and anisotropy. In this paper, the characteristic of contourlet transform is analyzed combined with synergetic pattern recognition. A new fusion method based on contourlet transform for prototype vectors generation is proposed. The coefficients structure and the framework's fusion procedure are given in detail to get the prototype vectors. Lastly, a synergetic classification algorithm based on prototype modify with rough set methods is presented from the view of traditionary cell characteristic parameter combined with synergetic pattern recognition. Which is focused on prototype modify from eigenvalue. The essence of Rough set theory is a mathematic tool describing imperfection and uncertainty, can effectively analyze and deal with those imprecise. The division matrix of rough set can get the best reduce result, and furthermore dynamic rough set method is applied and optimal non-linear features are got as prototype vectors. The optimal prototype vector which fit mostly nasopharyngeal carcinoma cell images recognition could be selected from the experiment result on different reduce result by the way of synergetic pattern recognition method.The synergetic recognition of nasopharyngeal carcinoma cell images is also focused on optimize of synergetic neural network. The order parameter is the determinant of synergetic systems-ordering. The transform method of order parameter can use the neural network self-learning ability to improve recognition performance of systems. A model of order parameters transform based on orthogonal polynomial approximating was presented, which can figure out a group of linear transformation parameters for order parameters using self-learning power of synergetic neural networks. A weights-direct-determination method is proposed which could immediately determine the neural-network weights in the training process. Experiment shows that the new algorithm can effectively search the reconstruction parameters and the recognition ability of system is improved. The recognition performance of synergetic neural network could improve by adjusting parameters in the neural network system. The parameters of synergetic neural network are optimized to improve the recognition effect by sufficiently using the self-learning abilities of synergetic neural network. Differential evolution is an effective searching algorithm for global approximate optimal solution, which has the characteristics of convergence fast to better solution. An algorithm of parameters optimization based on differential evolution was proposed. This new algorithm is used to search the global optimum attention parameters of SNN in the corresponding parameter space. Fitness mean square variance is adopted to modify searching speed and searching precision in the adaptive manner, because the parameters of differential evolution algorithm are hard to adopt dynamically, and the way of fitness mean square variance could helps improving the optimizing abilities of the algorithm. The new algorithm has better parameter searching abilities, both globally and locally, and can hardly been trapped into local extreme. A reduction parameter model is applied in this algorithm which improves the recognition ratio of the synergetic neural network system effectively.Invariance method is an important aspect of Synergetic Pattern Recognition research. Usually there are deformation between test pattern and prototype pattern. A synergetic invariance algorithm is proposed in this paper, which is based on alternant iterative match. The question of match is converted to question of function optimization in synergetic neural network. A potential energy function optimization algorithm which based on conjugate gradient method is proposed, and the optimum parameters of test pattern and affine transform are gotten by the way of alternant iteration. The nationalization of test pattern is equivalent to nationalization of prototype pattern in synergetic neural network. The right pattern can be gotten by the dynamic evolvement of order parameter.