Research On Medical Image Repair Algorithm Based On Tensor Completion

With the development of modern science and technology, medical image processing has become indispensable to people’s life. Medicine subject is closely related to human health, it’s importance is self-evident. The doctor’s diagnosis is mainly based on CT and MRI image at present. However, in the process of image acquisition or human factors will cause the acquisition image incomplete, which directly affect the medical diagnosis on disease. In recent years, extensive research is caused on the scholars of medical image restoration problems. Inspired by compressed sensing, a low rank matrix approximation produce a good effect in the field of image restoration. However, medical CT, MRI image is different from the ordinary optical image, it is an organ of the tomography, typically contain hundreds of or even thousands of images. Using the traditional image restoration algorithm caused a lot of times and laborious, and the effect is not satisfactory. In order to solve this problem, tensor completion algorithm is applied to medical image restoration. The main work is as follows:(1) The current research status of image restoration is introduced. Through comparative analysis found that the traditional image restoration algorithm is not suitable for medical image restoration. It is summarized the research of tensor completion problems and combined with the characteristics of medical image. The tensor completion algorithm is applied to the field of medical image restoration. The experimental results show that this method has a high application value.(2) A restoration algorithm for medical images based on low rank tensor completion of the weighted nuclear norm is proposed in this paper. The nonnegative constraint is added to remove the negative elements of the image tensor. Because the tensor structure and the size of each mode is not exactly the same, teach mode is linked by the weight parameters. The tensor completion problem is transformed into a regular optimization problem with weighted constraints. Finally using the singular value threshold method solve the regular optimization problem. Experiments were performed on the medical image. Compared with the HaLRTC algorithm, it greatly reduce the repair time and raise better restoration result.(3) A restoration algorithm for medical images based on tensor completion of Tucker decomposition and rank minimization technology is proposed in this paper. It combined the Tucker decomposition with the rank minimization technology. The nonnegative constraints is added to remove the negative elements of the image tensor. The general Gauss prior probability is added. We used the Chen’s MGE framework to capture the manifold structure of the original data in the absence of the potential complement elements in the latent image. The maximum a posteriori probability formula fixes the problem into a convex optimization problem with constraints, finally using the Lagrange algorithm augmented optimization. The algorithm is applied to medical image reconstruction, compared with the HaLRTC algorithm generate satisfactory results.This paper presents two kinds of medical image restoration algorithm based on tensor completion. The experimental results show that the algorithm has high research value. However, the only treatment research is currently low rank objects, or must be based on some kind of auxiliary factors. To find a universal medical image restoration algorithm still need researcher’s hard.