The Research On The Method Of Cervical Cell Image Segmentation And Recognition

The quantitative analysis and automatic recognition of cervical cell image utilizing computer technology and cervical cytological diagnosis have significant practical value and application prospect on the screening and diagnosis of cervical precancerous lesion and cervical cancer. Due to the cervical smears slice-making and staining technique differences, the background complexity, the cell form diversity and irregularity and the cell overlap making it difficult to process and recognize the cervical cell. image. The research on the automatic cervical cell image recognition has developed not so many and urgent need breakthrough and improvement.Based on the previous research, we make a systematical study on the techniques of cervical cell image segmentation, characteristic parameters computation and selection and cell image recognition by applying the depth research on image analysis, pattern recognition technique and cytological pathology knowledge. The main contents of this thesis can be summarized as follows:According to the structure of the cell, two independent level set functions have been constructed to approach the borders of the cell composition based on the Chan-Vese model with intra-region coherence and inter-region diversity properties. Through defining and improving the evolution equation of level set function on the basis of gray and area differences among nuclei, cytoplasm and background region, we can convert an three-region segmentation problem to two two-region segmentation problems. While there may be more than one connected cell regions in region of interesting (ROI), a method of main connected cell region extraction has been introduced. The segmentation experiment on each types of cervical cell image shows that by means of adjustment of weight values of two level set functions, any cell image with weak edges can be segmented precisely. Based on the success gray cell image segmentation, the color cell image segmentation has been proposed combined with the vector-valued Chan-Vese model. Focus on the problem that there exist cell adhesion and overlap in the image, a method combined with corrosion limit and concave area detection has been introduced to judge and separate the overlap cell image. The method has been operated on the overlapped cervical cell or nuclei images separation successfully. On the basis of precise segmentation of cell compositions, characteristic parameters that can be used in cell recognition are extracted, including morphology, color, optical density and texture features and 87 features are extracted in all. An approach is proposed to perform feature selection based on genetic algorithm. Define the fitness function on the principle of high reliability and distinction of feature subsets, generate the mutation probability adaptively, use two-point crossover and maintain optimal strategy, the genetic algorithm can be carried out to select the optimal features. In view of the randomness of original pop set generation, en evaluation criteria has been introduced to extract the qualified features to make up the optimal feature sets which can be used on the cervical cell image recognition.BP neural network with one hidden layer has been used to testify the efficiency of feature selection with genetic algorithm, the result shows that the recognition rate using the features selected by genetic algorithm is higher than the original features and it is effective to select the features by applying genetic algorithm. Considering that the generalization performance of single neural network is not high enough, we propose using neural network ensembles to recognize the cell image and generate the individual network by bagging algorithm. In order to decrease the error recognition rate of identifying malignant cells to normal ones and the overall error recognition rate, we quote a two-layer ensemble by cascading two neural network ensembles together and to finish the recognition task. The experimental results show that the overall error recognition rate of the two-layer neural network ensembles has been decreased significantly, the more important is that the error recognition of diagnosing malignant cells to normal ones has been decreased greatly.This thesis has making systematic research and improvement on the cervical cell image segmentation, characteristic parameters computation and selection and cell image recognition. The experiment results show that the methods we proposed in this thesis are effective to achieve the mission of quantitative analysis and automatic recognition of cervical cell image, and has establish a foundation on cervical cell image automatic analysis system.