Multi-mode medical image fusion method based on low-rank decomposition and sparse representation

A sparse representation, low-rank decomposition technology, applied in the field of image processing, can solve problems such as single function, achieve the effect of concise representation, convenient processing and good robustness

Active Publication Date: 2017-10-24
云南联合视觉科技有限公司
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Problems solved by technology

Compared with the above four types of algorithms, the other four fusion methods have relatively single functions

Method used

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  • Multi-mode medical image fusion method based on low-rank decomposition and sparse representation
  • Multi-mode medical image fusion method based on low-rank decomposition and sparse representation
  • Multi-mode medical image fusion method based on low-rank decomposition and sparse representation

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Embodiment 1

[0026] Embodiment 1: a multimodal medical image fusion method based on low-rank decomposition and sparse representation, the specific steps of the method are as follows:

[0027] Step1, two CT and MRI images with a pixel size of 256×256 to be fused (such as figure 2 , 3 shown), using matrix low-rank decomposition theory to perform low-rank decomposition to obtain low-rank partial images and sparse partial images respectively (that is, after CT image decomposition, a low-rank partial image A 1 and a sparse partial image A 2 , the MRI image is decomposed to obtain a low-rank partial image B 1 and a sparse partial image B 2 );

[0028] Step2. Use K-means singular value decomposition algorithm to select non-medical image sets (such as Figure 4 shown) to train a low-rank dictionary, and use the K-means singular value decomposition algorithm to perform low-rank decomposition on the selected non-medical image set to obtain a sparse part of the image set (such as Figure 5 sho...

Embodiment 2

[0033] Embodiment 2: a kind of multimodal medical image fusion method based on low-rank decomposition and sparse representation, the concrete steps of described method are as follows:

[0034] Step1, two MRI and PET images with a pixel size of 256×256 to be fused (such as Figure 7 , 8 shown), using matrix low-rank decomposition theory to perform low-rank decomposition to obtain low-rank partial images and sparse partial images respectively;

[0035] Step2. Use K-means singular value decomposition algorithm to select non-medical image sets (such as Figure 4 shown) to train a low-rank dictionary, and use the K-means singular value decomposition algorithm to perform low-rank decomposition on the selected non-medical image set to obtain a sparse part of the image set (such as Figure 5 shown) to train a sparse dictionary;

[0036] Step3, using sparse representation method to low-rank partial image A 1 , B 1 and the sparse part of the image A 2 , B 2 Perform sparse reconst...

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Abstract

The present invention discloses a multi-mode medical image fusion method based on low-rank decomposition and sparse representation. The method includes the following steps that: two different multi-mode medical images to be fused are subjected to low-rank decomposition, so that a low-rank part image and a sparse part image are obtained respectively; a KSVD (K-means singular value decomposition) algorithm is adopted to train a selected non-medical image set so that a low-rank dictionary can be obtained, and the KSVD (K-means singular value decomposition) algorithm is utilized to perform low-rank decomposition on the selected non-medical image set, so that a sparse part image set can be obtained, and the sparse part image set is trained, so that a sparse dictionary can be obtained; a sparse representation method is adopted to sparsely reconstruct the low-rank part image and the sparse part image, so that a low-rank reconstructed image and a sparse reconstructed image can be obtained; the sparse representation method is adopted to sparsely fuse the low-rank reconstructed image and the sparse reconstructed image, so that a fused image can be obtained; the difference values of the two multi-mode medical images and the sparse reconstructed image and the low-rank reconstructed image are calculated; and the difference values are added into the fused image, so that a final sparse fused image can be obtained. The subjective and objective evaluation indexes of the multi-mode medical image fusion method of the invention are better than the indexes of a traditional fusion method.

Description

technical field [0001] The invention relates to a multimodal medical image fusion method based on low-rank decomposition and sparse representation, belonging to the field of image processing. Background technique [0002] In the field of image processing, image fusion is a very promising research. Image fusion technology synthesizes a fused image by synthesizing useful information of multi-sensor images of the same scene or the same sensor images of different scenes. The synthesized fusion image has all the characteristic information of the former, which is more suitable for later processing and research. An efficient fusion method can handle multi-channel information according to actual needs. These advantages make image fusion very popular in many fields. In particular, in medical imaging, ultrasound images, X-rays, computerized tomography (CT), magnetic resonance imaging (MRI), digital subtraction imaging (DSA), positron emission tomography (PET), Various modal image ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50G06K9/68G06K9/62
CPCG06T5/50G06T2207/20221G06V10/75G06F18/25
Inventor 李华锋邓志华余正涛王红斌
Owner 云南联合视觉科技有限公司
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