Automatic Segmentation Algorithm Study Of Cardiac Images Based On Neural Network And Image Saliency

Currently, the cardiovascular disease is one of the main causes of human non-accidental death, it has the high incidence, and no fixed incidence of the law. Now,the cardiovascular disease gradually spread to young people, it threaten the healthy life of mankind always. The heart is the central organ of the cardiovascular system, and obtaining the physiological information of the heart is the key for the diagnosis and treatment of cardiovascular disease. CT imaging technology has the advantages of fast imaging speed, clear imaging and so on. It is the common means of cardiac examination.The segmentation of CT image is important for the diagnosis and treatment of cardiovascular disease. However, the heart is a solid organ, the common fault image sequence is difficult to completely show the physiological information of the heart, and for computer-aided diagnosis, interventional therapy guidance, cardiac surgery and other technologies usually need to get a complete cardiac anatomy. Three-dimensional visualization of medical images is the main means to find out the anatomy of the heart,while image segmentation is the data base to realize the visualization of human organs.For the segmentation of cardiac images, researchers at home and abroad have proposed a variety of segmentation methods,but most of the algorithms focus on the segmentation of the atria and ventricles, and difficult to meet the requirements of the whole heart segmentation. At the same time, due to the heart of the pulse and blood flow process is easily produce artifacts, border weakening and other issues to CT images, affecting the heart wall segmentation effect. Besides, the traditional manual segmentation method requires a lot of experience of medical staff, and the efficiency of segmentation is low. Therefore, the automatic cardiac segmentation has been a challenging hot issue in the field of medical image processing.Based on the segmentation and recognition of medical images, the thesis presents a new method based on neural network and image prominence, which is based on the difference between the cardiac images and other tissues in the slices is obvious, and there is a high similarity between adjacent slices in the cardiac CT image sequence.Then, reconstructed the cardiac images with 3D visualization.The main contents and innovations of this thesis are as follows:1. Using visual significance techniques to achieve cardiac segmentation based on CT images. Taking an image significance detection algorithm to calculate the CT image,and the original CT image is segmented by the significant image to obtain the complete cardiac image.2. The segmentation task is decomposed into two parts: location and segmentation,and using convolution neural network and stack noise reduction self-coding network to achieve the localization and segmentation. Constructed the convolution neural network based on the excellent performance in image classification and target recognition,achieved the positioning function of heart in the image. Then, the image of the original heart CT is cut and the partial non-target area is removed by the positioning result.Besides constructed a stacked denoising autoencoder, and trained it by manually dividing the image to achieve the classification and recognition of the pixels belonging to the heart tissue in the CT image of the heart. Finally, the segmentation of the cardiac image is completed based on the classification result.3. Compared the results of this segmentation algorithm with the artificial segmentation results, and got the quantitative evaluation of these two results. Finally,visualized the segmentation results with two kinds of surface rendering and volume rendering visualization algorithms.