Research On Medical Image Segmentation Which Is Based On Active Contour Model

Medical image segmentation which extracted special tissue information from medical images is a very important step.Currently,image segmentation methods are quite a lot,because of the medical image itself has gray inhomogeneity,imaging equipment noise interference and the complexity of the human anatomy,the general image segmentation methods are not ideal for medical image processing.Based on the analysis of classical segmentation algorithms which are based on active contour model.In view of the existing problems of RSF(Region-Scalable Fitting)model,two improved algorithms are proposed.1?Proposed an improved model of the RSF(Modified Region-Scalable Fitting,MRSF).The RSF model is an active contour model which is based on image gradient information,there is no use of the global information of the image,and not taking any measures to improve the speed of separation,therefore,there are defects such as being less divided and the slow convergence of outline during the segmentation of certain medical images by the RSF model.In order to solve the problems of RSF model,the MRSF model takes the following measures.First,k-means is employed to process the medical image globally,then a new kernel function replaces the Gaussian function.On the basis of the new kernel function,a new energy function is re-established,and the internal energy is introduced into the level set model as a penalty function.Experiments show that,both in segmentation accuracy and segmentation speed,MRSF model is superior to traditional segmentation model.2?Came up with an improved RSF model which is based on modified K-means(Region-Scalable Fitting based on Modified K-means,MKRSF).The MRSF model uses the K-means as a preprocessing step,however,the segmentation result of the model is largely dependent on the results of the K-means processing,the effect of noise on the K-means is more serious.Therefore,in order to reduce the dependency of the segmentation results on global processing and enhance the ability of the model to resist noise interference,the MKRSF model's approach is to integrate the improved K-means method into the energy functional as a global term for the total energy functional,the improved of the K-means is considering the pixel space position information;in order to improve the segmentation speed,the energy functional of the RSF model which is improved by the new kernel function as the local term of the total energy function;the length of the curve and the defined penalty term as the constraint of the total energy function;under the adjustment of the parameters and the combined action of each item,the curve converges around the target.Experiments show that,the MKRSF model not only has the same segmentation accuracy and speed as the MRSF model,but also has a strong ability to resist noise interference.