Computed tomography (CT) image and magnetic resonance (MR) image fusion method

Abstract

The invention discloses a computed tomography (CT) image and magnetic resonance (MR) image fusion method capable of improving program execution efficiency. The invention adopts the technical scheme that: direct statistics is conducted to the gray-level histograms of the images; and the contrast and the second order contrast of the source images are calculated according to the gray-level histograms. The adopted algorithm is as follows: the second order contract of corresponding pixels between the source images is compared, a second order contrast value with a bigger absolute value is kept, then the contrast of a fused image is calculated according to a contrast mode and the second order contrast value, and finally the gray value of the fused image is calculated according to the contrast value and the gray mode of the fused image. The CT image and MR image fusion method has the beneficial effects that: the contrast and the second order contrast of the source images are calculated according to the gray-level histograms, so that the source images do not need to be converted, the computational load is significantly reduced, the program execution efficiency is improved, and the subjective effect of the fused image is significantly improved.

Description

technical field [0001] The invention relates to the fusion of medical imaging images, in particular to a fusion method of CT images and MR images. Background technique [0002] Medical imaging technology is a comprehensive and practical subject field that integrates various disciplinary achievements and advanced technologies. Various modes of medical images provide doctors and researchers with rich, intuitive, qualitative and quantitative human physiological information from a visual perspective, and become an important technical means for diagnosing various diseases. Since different modes of equipment have different sensitivities and resolutions to the human body ranging from tissues to molecules and atoms, they have their own scope of application and limitations. Computed tomography (CT) images have better spatial resolution and geometric characteristics, and have lower contrast to human soft tissues, but clearly reflect bones. Computed tomography (CT) images have clear ...

AI Technical Summary

Problems solved by technology

Since the 1980s, medical image fusion has gradually attracted the attention of the clinical medical community. At that time, some studies generally used relatively intuitive and simple fusion methods, such as weighted averaging pixel by pixel, filtering using logical operators, etc. Wait, the effect is often not ideal

There are two problems in this kind of multi-scale transformation image fusion technology: first, the algorithm is complex and time-consuming

The multi-scale transformation image fusion algorithm needs to perform two multi-scale transformations and one inverse transformation of the image, and these three image transformations take up most of the time

Second, only absolute information is transferred to the fused image, ignoring relative information

The current multi-scale transformation image fusion technology directly transfers the transformation coefficients to the fusion image according to various fusion schemes, ignoring the relative information, which directly leads to the decrease of the contrast of the fusion image.

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