Research On Pulmonary Node Segmentation Based On Fuzzy C-means Clustering

Improving the survival rate of lung cancer patients has been a worldwide problem.Computer-aided detection(CAD)technique can help doctors to diagnose quickly,and then help patients to detect the disease as early as possible and treat it in time,thus effectively improve the survival rate of lung cancer patients.Therefore,the breakthrough of related technology is one of the keys to solve this problem.In recent years,with the development of artificial intelligence technology,digital image processing technology and medical imaging technology,computer aided detection(CAD),technology has become one of the key technologies in biomedical engineering.The rapid analysis and detection of lung CT images can be realized by using computer aided detection(CAD)technique,and the accuracy of lung nodule detection can be improved effectively.During the process of collecting lung CT images,images are easily influenced by the structure and noise of human lungs,so the images collected are often uncertain.Medical image has high requirements for segmentation accuracy,although there are a lot of existing segmentation methods,but not perfect.Introducing fuzzy theory into the fuzzy clustering algorithm of clustering analysis can effectively solve the problem of image segmentation.Therefore,it is of great theoretical and practical significance to study the relevant fuzzy clustering theory and improve the accuracy of the corresponding image segmentation algorithm.The main work of this paper is as follows:1)The theory and algorithm of image segmentation are studied.The technology of image segmentation is studied,and the advantages and disadvantages of the algorithm are analyzed in detail.2)The theoretical knowledge of clustering analysis and fuzzy set is studied.The fuzzy C-mean algorithm and the related improved algorithm are studied,and the steps of the algorithm and the selection of parameters are described in detail.3)A new weighted spatial function based on image local spatial information and gray level information is proposed to overcome the shortcomings of the traditional fuzzy C-mean(FCM)clustering algorithm,and an improved local adaptive fuzzy algorithm is obtained.The specific steps of the improved algorithm are explained in detail,and the algorithm is simulated and compared many times.By analyzing the experimental data,the validity of the proposed new algorithm in dealing with lung images is confirmed.4)The proposed algorithm is applied to the segmentation of pulmonary nodules.Through many experiments,it is proved that the proposed algorithm is more effective than other traditional FCM algorithms in using the neighborhood information and gray information of lung images,the result is better.