Rearch And Application Of The Level Set Method Based On Scale-space Theroy Framework

In medical images, the three-dimensional images of CT vessels are widely available in clinical diagnosis and treatment, and that have been an important basis for disease diagnosis. The three-dimensional image is reconstructed by the result that got from the edge detection of two-dimensional medical image, so the two-dimensional medical image edge detection is a very important research subject.Aiming at the shortcomings of the classical edge detection algorithm for low-noise immunity and the complexity of the Gaussian multi-scale edge detection for selecting the different scale, this paper proposed the integration of fuzzy enhancement algorithm and anisotropic smoothing algorithm for image preprocessing, and then conducted the zero-crossing edge detection based on a single scale. In this way, it completed the denoising and smoothing operation, and reached the purpose of extracting the edge. This method got better result for the image which has higher contrast pixels, but because of inherent characteristics of medical images, for example, they are always vague, this paper think that the fuzzy enhancement algorithm is not appropriate for medical image preprocessing. So it proposes a new edge detetion method that combines the level-set denoising method with the zero-crossing edge detection method based on scale-space, and then this method is applied to detect the edges of medical images. In this method, level set method is adopted for image denoising, filtering out some of the noise points, then the details of the image have been obscured, so the anisotropic smoothing theory is introducted, which can conduct smoothing on denoised image. The method can not only smooth out some useless details, but also suppress the noise and high-frequency interference components. And finally it uses a single scale zero-crossing edge detection method based on Gaussian kernel to detect the image edge. In the process of this method proposed, this algorithm is compared with the classical edge detection operators. The experimental results show that this method can solve the contradiction between the accuracy of image edge extraction and the suppression of image noise, and it can get better results than other methods for the medical images.