Contourlet domain multi-modal medical image fusion method based on statistical modeling

Abstract

The invention discloses a Contourlet domain multi-modal medical image fusion method based on statistical modeling, mainly for solving the problems of difficulty in balancing spatial resolution and spectrum information during medical image fusion. The realization steps comprise: 1), performing IHS transformation on an image to be fused, and obtaining brightness, tone and saturation; 2), respectively executing Contourlet transformation on a brightness component, and estimating the CHMM parameters of a context hidden Markov model of a high frequency sub-band by use of an EM algorithm; 3), a low frequency sub-band employing a fusion rule of taking the maximum from area absolute value sums, and the high frequency sub-band designing a fusion rule based on a CHMM and an improved pulse coupling nerve network M-PCNN; 4), a high frequency coefficient and a low frequency coefficient after fusion executing Contourlet inverse transformation to reconstruct a new brightness component; and 5), obtaining a fusion image by use of IHS inverse transformation. The method provided by the invention can fully integrate the structure and function information of a medical image, effectively protects image details, improves the visual effect, and compared to a conventional fusion method, greatly improves the quality of a fusion image.

Description

technical field [0001] The invention relates to a multimodal medical image fusion method in Contourlet domain based on statistical modeling, which is a fusion method in the technical field of medical image processing and is widely used in clinical medical diagnosis and treatment. Background technique [0002] As a research branch and research focus in the field of image fusion, with the rapid development of medical imaging technology, multimodal medical image fusion has become a research hotspot at home and abroad. Different medical images can provide different information about the relevant organs and tissues of the human body. For example, CT and MRI images with higher resolution provide the anatomical structure information of organs, while SPECT and PET images with lower resolution provide information about the viscera. Functional metabolism and blood flow information of organs. In order to make up for the lack of single-modal image information and provide medical worker...

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Problems solved by technology

[0007] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose a multimodal medical image fusion method based on statistical modeling in Contourlet domain, to solve the problem of insufficient spatial resolution of the fused image obtained by the existing multimodal medical image fusion method. The problem of high or spectral information distortion, and fully integrate the structural information and functional information of different modal medical images, effectively protect image details, enhance image contrast and edge contours, improve its visual effect, and improve the quality of fusion images

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