Application Of Medical Image Segmentation Based On Variational Level Set Method

Image segmentation is an important part of image processing,and it is the underlying technology of image processing.After years of development,image segmentation has been widely used in our daily work and life.With the development of medical imaging,medical image segmentation has become a hot and difficult topic in image segmentation research.Medical images are easily interfered by a variety of internal and external factors,which make the images complex and diverse.Primitive medical images usually include features such as uneven gray edges,high noise,and multiple targets.Traditional image segmentation algorithms are difficult to segment effectively and require multiple uses.The method is fitted to target segmented medical images of different characteristics.In recent years,the variational level set(LSM)has attracted more and more attention from experts and scholars due to its low complexity and easy to fit other algorithms,and it has been applied to devetopment very quickly.However,the variational level set algorithm also has the problems of poor stability,poor robustness,and large computational complexity when segmenting original medical images containing complex conditions.Therefore,the medical image segmentation research based on variational level set method has high application value and important practical significance.On the basis of reference to many documents,this article has investigated medical image segmentation research based on improved level set method,and proposes several improvement methods:Segmentation of medical images based on multiresolution double level set algorithm?Double level set algorithm based on NL-Means denosing method for brain MR images segmentation etc.And related intelligent image processing system is bewrote and designed.The main research contents and innovation points are as follows:1?This paper expounds the background and significance of medical image segmentation technology research,states the mathematical description of the medical image segmentation techniques,introduces the present situation of medical image segmentation technology research,also,the development of the medical image segmentation technology and difficulty is discussed.This paper describes basic model and common model of the level set method in mathematical way.2.This paper proposes a novel multiresolution double level set algorithm to medical image,which have a large amount of intensity inhomogeneities and complicated background,and can not be separated completely by traditional level set.First of all,the algorithm gets the coarse scale image by analyzing the image with wavelet multiscale decomposition.Then,the algorithm identifies multiple targets by segmenting the analysed results in terms of improved double level set model.In order to deal with the effect of intensity inhomogeneities on the medical image,the algorithm introduces a bias fitting term into the improved double level set model and optimizes the coarse-scale segmentation result.3.This paper proposes a novel double level set algorithm based on NL-Means denosing method for brain MR image,which have a large amount of noise and complicated background,and can not be separated completely by traditional level set.First of all,the algorithm gets the denoised image by analyzing the image with NL-Means denosing method.Then,the algorithm identifies denoised image by segmenting the analyzed results in terms of improved double level set model.In order to deal with the effect of intensity inhomogeneities on the medical image,the algorithm introduces a bias fitting term into the improved double level set model and optimizes the denosing method result.4.This paper summarizes the advantages and disadvantages of the algorithm in the paper,and proposes some improvements to provide guidance for future learning.