Image noise estimation method

Abstract

The invention provides an image noise estimation method. The image noise estimation method comprises steps: an image is inputted, linear structure detection is carried out on the image and a linear structure diagram is obtained; a background region of the image is extracted according to the linear structure diagram; the number of pixels of the background region and the size of a predetermined threshold are compared, and if the number of pixels of the background region is larger than or equal with the predetermined threshold, noise estimation of the image is calculated according to the gray value of the pixels in the background region; and if the number of pixels of the background region is smaller than the predetermined threshold, noise estimation of the image is calculated according to the gray value of the pixels in a smooth region in the linear structure diagram. The background region can be automatically, quickly and accurately extracted on the basis of linear structure detection transformation forms, noise estimation is obtained on the basis of the background region, interference of high-frequency details in the image is little, and stability and accuracy are facilitated.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to an image noise estimation method. Background technique [0002] Medical images are important reference information for modern clinical diagnosis and treatment, and the image quality is directly related to the effect of diagnosis and treatment. During the process of generation, transmission and storage, medical images are inevitably disturbed by various noises due to the influence of imaging objects and imaging equipment. In actual clinical applications, in order to provide clinicians with more accurate auxiliary diagnostic information, it is generally necessary to perform post-processing work on images such as denoising, segmentation, clustering, and restoration, and many of these processing algorithms require noise Variance is a known parameter, therefore, how to quickly and accurately estimate the noise level in the image is an important link in the process of medical i...

AI Technical Summary

Problems solved by technology

This type of method only considers the grayscale information of the image, so it is difficult to accurately extract the background area when there is no obvious grayscale difference in the image or the grayscale range of each object has a large overlap

The edge-based background extraction method achieves the goal of separating the target from the background by detecting the edges between different regions. For the case of discontinuous edges, using edge detection operators such as Prewitt operator, Canny operator, Sobel operator, etc. cannot The closed boundary curve between the target and the background is obtained, so the robustness of this background extraction method cannot be guaranteed

Combined background extraction method of region and edge, and other algorithms such as region growing method [Pal N R, Pal S K. Entropic thresholding [J]. Signal processing, 1989, 16 (2): 97-108], genetic algorithm [Wu Chengke, Liu Jing. Genetic Algorithm Method for Image Segmentation[J]. Journal of Xidian University, 1996, 23(1): 34-41], Fuzzy Clustering [Coleman G B, Andrews H C. Image segmentation by clustering[J]. Proceedings of the IEEE, 1979, 67(5): 773-785], Snake model, etc., although the robustness of the above method is slightly stronger, but the calculation takes a lot of time, so they are not considered.

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