Research On Algorithms Of MR Image Segmentation Based On DICOM Document Pattern

In the field of digital image processing, image denoising and image segmentation are the most basic and the most important technology. Medical image is one of the most complex and diverse in the image, lead to research on medical image denoising and segmentation become a difficult and challenging task. Medical image segmentation is early treatment of normal and diseased tissue for quantitative analysis and identification,at the same time, it is the important process of computer aided diagnosis as well.So accurate segmentation of medical images can make accurate clinical diagnosis for the medical workers,it is very important.Base on improved NL-Means algorithm for medical image denoising, through change the denoising key role of the weighted kernel function, Both to make full use of the image redundancy information,and be able to keep the useful information in the reduce the image denoise.So to deal with the medical images with more details, Shows its superiority. In recent years,research on Algorithms of medical image segmentation Based on level set is a hot research topic in the field of image segmentation,and show a good performance in image segmentation. The medicine MR image as the research object in this paper,improving the traditional NL-Means algorithm and level set algorithm,and verify the effectiveness of the algorithm through a series of medicine MR image denoising and segmentation experiment.In this dissertation, the main work is as follows:(1)We come up with an algorithm base on improved NL-Means for medical image denoising. First introduces the traditional NL-Means denoising algorithm,it can make full use of the image redundancy information,there is a good effect. In order to make the traditional NL-Means algorithm can better deal with medical image processing,combining with the intrinsic characteristics of the wavelet kernel function and gaussian kernel function,changeing the weighted kernel function that combining the wavelet kernel function and the gaussian kernel function as the weighted kernel function,in this paper. By contrast the traditional model of medical image denoising experiment and model in this paper, Shows that this model is higher than the traditional algorithm in the Peak Signal-to-noise Ratio(PSNR). (2)Combining the FCM algorithm with the NL-Means algorithm. The traditional FCM algorithm is a classic medical image segmentation algorithm,but particularly sensitive to the noise.However,the NL-Means algorithm has a good denoising performance.First by the NL-Means algorithm smooth image,and using the FCM algorithm for segmentation. By comparing the FCM algorithm and this algorithm, this algorithm can be proved that owing more noise resistance.(3) We come up with an algorithm research on Algorithms of medical image segmentation based on level set. First introduces the traditional level set segmentation algorithm, then describes the Li and He Chuanjiang and so on such as improved algorithm for traditional algorithm.Although their improved algorithm without to initialize level set function, still need to carefully set the related parameters and some artificial participation,then get an ideal level set segmentation result. The FCM algorithm segmentation result directly control level set evolution equation in this paper, and related parameters settings may also directly from the results. At the same time this paper do not introduce the gradient factor,in comparison, this paper owe more noise resistance. By several groups of segmentation experiments, proveing that this algorithm has better segmentation accuracy and robustness.