CT image denoising method based on self-adaptive median filtering

Abstract

The invention discloses a CT image denoising method based on self-adaptive median filtering, belongs to the technical field of medical image processing, and is particularly suitable for CT image denoising of new coronal pneumonia. The method comprises the steps of selecting a square filtering window with the size of n * n, comparing the adaptive maximum value and the adaptive minimum value of the gray value in the window with the gray value f (i, j) of the current pixel point, judging whether the current pixel point is a suspected noise point or not according to a first threshold T0, and if so, further accurately judging whether the current pixel point is a noise point or not according to a second threshold T1, if the current pixel point is not the suspected noise point or the noise point, traversing the next pixel point in the window; processing noise points through a center weighted median filtering method; and finally, outputting the CT image after median filtering and denoising. Image details are better protected while image denoising is kept; the problem that deviation exists in information transmission of a traditional entropy weight method is corrected by improving the entropy weight method, the optimal weight of center weighted filtering is determined through contribution values of all evaluation indexes to the denoising effect, and therefore the optimal denoising effect is achieved.

Description

technical field [0001] The present invention relates to the technical field of image signal processing, in particular to a CT image denoising method based on adaptive median filtering, which is suitable for filtering processing of pneumonia CT images, especially suitable for CT image denoising of new coronary pneumonia, and belongs to medical image processing technology field. Background technique [0002] The lesions of COVID-19 are mainly characterized by various forms of ground-glass opacity, or accompanied by consolidation, and CT images will be disturbed by pulse noise during transmission and acquisition. The early stage of COVID-19 has no obvious changes in lesions, the number of lesions is small, the range of lesions is small, and the density is low. Impulse noise interference is likely to cause the problem of missed diagnosis of patients with early stage COVID-19. [0003] For the traditional CT image pulse noise denoising algorithm, the median filter is easily affe...

AI Technical Summary

Problems solved by technology

In the early stage of the new crown, the changes of the lesions are not obvious, the number of lesions is small, the range of the lesions is small, and the density is low. Impulse noise interference is likely to cause the problem of missed diagnosis of early new crown patients

[0003] For the traditional CT image pulse noise denoising algorithm, the median filter is easily affected by the window size. When the window size is too small, the median filter is easily disturbed by the surrounding pixels, so that the gray value of the original signal point is replaced by noise. The gray value of the point makes the image denoising ability limited, and the noise cannot be removed correctly

When the window size is too large, the interference ability of noise points will be greatly reduced, which can greatly improve the image denoising ability, but will cause image details (such as edges, lines, corners, etc.) to be destroyed

Therefore, traditional median filtering is difficult to perform well in image denoising and preserving image details.

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