Gray scale correction method in image mosaic

Abstract

A grayscale correction method in image mosaic, comprising: selecting two adjacent images as images to be mosaic; defining a region of interest in the images to be mosaic; selecting a reference image from the images to be mosaic; The gray value of the region of interest of the reference image is corrected for the gray value of the region of interest of the non-reference image. The grayscale correction method in image mosaic of the present invention can generate a corresponding correction scheme based on image features, and has self-adaptability. This method can complete the grayscale correction without losing the contrast of the human body area of ​​the original image, and even improving the contrast of the original image.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a gray scale correction method in X-ray image mosaic. Background technique [0002] When using X-rays to observe a large area of ​​the human body, a single image is not enough to cover the entire lesion area, so multiple images need to be taken, and the multiple images are spliced ​​into a large image by splicing and fusion methods, so that doctors can analyze the lesion as a whole . Due to the differences in various parts of the human body, different doses of X-rays are used when shooting different parts in order to achieve a better display effect for different parts obtained by shooting. Therefore, the difference in exposure dose and the difference in various parts of the human body will lead to large differences in gray levels between images of different parts, and this difference will of course also be reflected in the stitched image. This difference in gray level will adver...

AI Technical Summary

Problems solved by technology

This difference in gray level will adversely affect the doctor's diagnosis and analysis of the patient's condition, increasing the false detection rate

[0003] In the prior art, the brightness mapping method is used to eliminate the gray level difference in the spliced ​​image. The brightness mapping method realizes the spliced ​​image by adjusting the window width and window level of each image without modifying the gray value of each image. The gray scale effect is consistent. The defect of this method is that when each image is taken by different doses of X-rays, because the human body has different degrees of attenuation of X-rays with different doses, simply adjusting the window width and window level cannot make the The overall grayscale of the spliced ​​image tends to be consistent

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