Medical image enhancement method combining fuzzy set and fractional differential

Abstract

The invention provides a medical image enhancement method based on combination of a fuzzy set and fractional differential. The method comprises the following steps: decomposing an original medical image by using Haar wavelet transform to obtain a low-frequency sub-band component and a high-frequency sub-band component of the image; enhancing the extracted low-frequency sub-band components by adopting a fuzzy set method of an adaptive threshold to obtain enhanced low-frequency self-band components; constructing a mask by using fractional differential, and carrying out convolution operation on the mask and the high-frequency band component to obtain an enhanced high-frequency band component; and through wavelet reconstruction, performing combined reconstruction on the enhanced low-frequencyself-band component and the enhanced high-frequency band component, and recovering the enhanced image. Wavelet transform is utilized to decompose the image, different extracted frequency bands are enhanced by using different methods, the contrast of the enhanced image is higher, more texture details of the image are depicted, and edge contour details become clearer.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a medical image enhancement method combining fuzzy sets and fractional differentiation. Background technique [0002] In the process of digitalization of medical imaging, due to the influence of various factors such as imaging equipment, shooting environment, and physical methods, the image is often accompanied by problems such as large noise, low contrast, and blurred tissue edges. These factors greatly affect the doctor's diagnosis of the disease. With the development of computer technology, digital image processing has been applied to the field of medical diagnosis. Using computer technology to improve the quality of medical images can obtain better visual effects and promote medical diagnosis. Therefore, the enhancement of medical images is particularly important. [0003] Medical images provide important information basis for clinical diagnosis. To a large extent, low-quality...

AI Technical Summary

Problems solved by technology

In medical images, the tissue edges and corners of the human body cannot be accurately positioned, and it is difficult to strictly distinguish different areas of the image. In addition, different tissue structures correspond to relatively similar grayscale features, which cannot be accurately divided during processing.

Due to this uncertainty, a large number of studies are keen to apply fuzzy set theory to image processing, but the simple fuzzy set enhancement method is difficult to effectively enhance complex medical images, and the fuzzy set method has limited ability to enhance the effect. Unable to achieve good enhancement

In addition, due to various factors such as environmental interference, imaging equipment, and photographer interference during the imaging process of the machine, excessive noise and blurred images appear in medical images, which seriously affects doctors' understanding of the condition. Analysis of Diagnostics and Research of Medical Imaging

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