| Medical cell image segmentation is a key step in medical image analysis and recognition.Medical cell image segmentation can quickly and accurately find the cell nucleus and micronucleus cells,etc.,which brings convenience to subsequent cell image processing,such as feature extraction,target detection and information analysis.In this paper,human oral mucosa cell image is taken as the research object,and cells are segmented by Markov Random Field medical cell image segmentation algorithm and deep network medical cell image segmentation algorithm based on capsule network.The problems such as the uncertain clustering number of Markov Random Field medical cell image segmentation algorithm,the decrease of precision caused by the loss of some cell features due to pooling of convolutional neural networks(CNN)and the unsatisfactory segmentation effect caused by the low segmentation efficiency,etc.are solved.It makes accurate discrimination for the later image analysis,finds the normal cells and the lesion cells,and finds the pathological root,so as to apply the medicine to the case.Firstly,the research background,significance and current research situation of medical cell image segmentation are described,and then the research content,objective and structure of this paper are summarized.Secondly,the method of image segmentation is introduced.The algorithm of medical cell image preprocessing is described,including color image graying and image de-noising.Image de-noising is carried out by using fusion filtering method to solve the noise generated in medical cell image.Finally,this paper studies the Markov random field medical cell image segmentation algorithm and expounds the application of convolutional neural network to medical cell image segmentation algorithm.Aiming at the problem inaccurate segmentation of medical cell images caused by the influence of subjective factors and the artificial initial parameter setting of convolutional neural network,this paper proposes the Markov random field medical cell image automatic segmentation algorithm based on Chinese restaurant model,which can automatically obtain segmentation areas for smoother and more accurate images of medical cells.Compared with other segmentation methods,the results show that the Markov random field medical cell image automatic segmentation algorithm based on Chinese restaurant model has higher accuracy.the pooling of convolutional neural network leads to the loss of some cell features and further gives rise to decrease of accuracy and low efficiency,which will cause poor segmentation effect and other matters.Directing at these problems,a deep network medical cell image segmentation algorithm based on capsule network is proposed.The method can extract rich features with small amount of training data.Through experimental comparison,it is found that the deep network medical cell image segmentation based on capsule network does not require a large number of samples and training times when compared with the full convolutional network(FCN)segmentation algorithm,whose training time is 2.3 times higher than that of FCN. |
