Multi-modal medical image fusion method

Abstract

The invention discloses a multi-modal medical image fusion method, which comprises the following steps of 1) source image decomposition: performing two-stage decomposition discrete stationary wavelet transform on at least two source images, 2) source image fusion: fusing the at least two source images through seven enhanced radial basis function neural networks, and 3) inverse wavelet transform: performing inverse wavelet transform, through the inverse wavelet transform, converting the seven fusion sub-bands into a fusion image. The method has the advantages that through translation of the invariant multi-scale transformation operator, enough direction information is provided, and the algorithm is relatively simple and easy to operate. In order to enhance the self-learning ability of the neural network, in combination with medical image fusion, pixel values, regional energy, pixel gradients and regional average gradients are adopted to form an input layer of the neural network, information of target feature points is accurately extracted, and meanwhile, information loss is avoided.

Description

technical field [0001] The invention relates to the technical field of image fusion, in particular to a multimodal medical image fusion method based on discrete stationary wavelet transform and enhanced radial basis function neural network. Background technique [0002] Image fusion technology is to synthesize different source images into a new image through a certain technology or method. Rather than simply superimposing all image data, the process performs targeted analysis and processing of the target image through one or more algorithms. For multimodal medical image fusion, two (or more) medical images from different imaging devices are combined, and an algorithm is used to analyze the advantages or complementarities of each image, so as to further obtain effective Information is fused. With the development of medical imaging technology, computed tomography (Computed tomography, CT), magnetic resonance imaging (Magnetic resonance imaging, MRI), positron emission tomogr...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems existing in the prior art, the present invention provides a general feed-forward neural network fusion technology based on feature point prior to solve The technical difficulty of incomplete extraction of useful tiny information from images to be fused and easy loss

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