Contourlet transformation-adaptive medical image fusion method based on non-sampling

Abstract

The invention relates to a contourlet transformation-adaptive medical image fusion method based on non-sampling and belongs to the field of image processing. The method comprises the steps that firstly, a source image is subjected to arithmetic average filtering and then is decomposed through an orthogonal 9-7 wavelet filter and a pkva filter during non-sampling to obtain low-frequency sub-band coefficients and all band-pass direction sub-band coefficients; secondly, the low-frequency sub-band coefficients are selected and fused according to the edge information maximum criterion, all the band-pass sub-band coefficients are selected and fused through an adaptive PCNN model based on a visual neuron model; lastly, a final fused image is obtained by means of inverse transformation of NSCT. According to the contourlet transformation-adaptive medical image fusion method based on non-sampling, the algorithm is very effective and correct, the edge and space texture information of the fused image is clear, color distortion is low, the false contour phenomenon does not exist, and feature information of the source image is well reserved.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a medical image fusion method based on a non-sampling contourlet transform (NSCT) adaptive pulse-coupled neural network (PCNN). Background technique [0002] Image fusion refers to the synthesis of information about images or image sequences of a certain scene obtained by two or more sensors at the same time or at different times, so as to generate a new, more comprehensive and accurate description of the scene. Image. [0003] With the rapid development of medical imaging technology, the image quality has been greatly improved. However, due to the different imaging principles of medical imaging technology, using one modality imaging technology alone often cannot provide enough information for doctors. It is usually necessary to fuse medical images of different modalities to obtain comprehensive and complementary information in order to understand the comprehensive information of...

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Problems solved by technology

2006: 420-424"Research shows that the Contourlet transform needs to downsample the image, which makes the Contourlet transform produce false contours due to the lack of translation invariance

Telecommunications Technology, 2003, 3: 21-24" shows that artificial neural networks have been widely used in image fusion, especially the pulse-coupled neural network formed by Eckhorn et al. The field of image processing is being widely studied, but the connection strength of traditional PCNN is usually constant, which greatly limits the automatic processing ability and universality of PCNN

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