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Medical image fusion method based on WEMD and PCNN

A medical image and fusion method technology, applied in the field of medical digital image processing, can solve the problems of fusion image edge and contrast distortion, poor adaptability, etc.

Inactive Publication Date: 2016-03-23
XIAN UNIV OF TECH
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  • Claims
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Problems solved by technology

[0007] The purpose of the present invention is to provide a medical image fusion method based on WEMD and PCNN, which solves the problems of poor adaptability of the multi-scale decomposition algorithm and serious distortion of the edge and contrast of the obtained fusion image in the prior art

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  • Medical image fusion method based on WEMD and PCNN
  • Medical image fusion method based on WEMD and PCNN
  • Medical image fusion method based on WEMD and PCNN

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Embodiment Construction

[0064] The present invention will be described in detail below in combination with specific embodiments.

[0065] The hardware environment used for implementation is: Intel Core i5 processor, 3.20Hz computer, 4GB memory, 512M graphics card, and the running software environment is: matlab2014a and Windows7 64-bit operating system. We have realized the method that the present invention proposes with Matlab software. The CT images and MRI images used in the experiment are from http: / / www.metapix.de / indexp.htm , the MRI-T1 weighted images and MRI-T2 weighted images used were from http: / / www.nlm.nih.gov / research / visible / getting_data.html , and have been strictly registered.

[0066] A kind of medical image fusion method based on WEMD and PCNN of the present invention, as figure 1 and figure 2 As shown, the specific steps are as follows:

[0067] Step 1. There are two medical images, respectively recorded as image A and image B, and image A and image B are registered respect...

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Abstract

The invention discloses a medical image fusion method based on WEMD (Window Empirical Mode Decomposition) and PCNN (Pulse Coupled Neural Network). The medical image fusion method based on WEMD and PCNN comprises the steps: performing a layer of WEMD for a source image, thus not only overcoming the problem that a traditional wavelet method is difficult to select a wavelet primary function and is poor in adaptation, but also effectively avoiding the gray scale speckle phenomenon of the BIMF (Bidimensional Intrinsic Mode Function) component obtained through a traditional BEMD (Bidimensional Empirical Mode Decomposition) method, and accelerating the decomposition speed; designing different fusion rules according to the image characteristics for the corresponding BIMF component and the residual component respectively, and providing visual effect for the fusion image maximumly, wherein the BIMF component utilizes the fusion rule based on an improved and simplified PCNN model to select the clear area of the image and the residual component utilizes the fusion rule based on the area energy to enhance the image detail; and finally obtaining the fusion result by performing WEMD inverse transformation for the fusion component. The medical image fusion method based on WEMD and PCNN solves the problem that in the prior art, the multi-scale decomposition algorithm is poor in adaptation and the edge and the contrast of the obtained image are serious in distortion.

Description

technical field [0001] The invention belongs to the technical field of medical digital image processing, and in particular relates to a medical image fusion method based on WEMD and PCNN. Background technique [0002] Medical image fusion technology is to properly process two or more medical image information that are different from each other and complement each other, so that the useful information in the image can be comprehensively expressed and displayed after processing, instead of the doctor's subjective artificial synthesis method. , so that doctors can analyze and judge the condition more accurately, and improve the efficiency and reliability of clinical diagnosis. [0003] Medical image fusion mainly includes fusion between functional imaging images and functional imaging images, anatomical imaging images and anatomical imaging images, functional imaging images and anatomical imaging images. Common fusion types include CT and MRI image fusion, MRI and MRI image fu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50G06N3/02
Inventor 秦新强胡凯胡钢
Owner XIAN UNIV OF TECH
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