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Brain CT/MR (Computed Tomography/ Magnetic Resonance) image fusion method for improved coupled dictionary learning on the basis of sparse representation

A coupling dictionary and sparse representation technology, applied in the field of image processing, can solve problems such as lack of flexibility and low time efficiency

Active Publication Date: 2017-09-22
ZHONGBEI UNIV
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Problems solved by technology

However, for each source image to be fused, a training dictionary is required, which is time-efficient and lacks flexibility.

Method used

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  • Brain CT/MR (Computed Tomography/ Magnetic Resonance) image fusion method for improved coupled dictionary learning on the basis of sparse representation
  • Brain CT/MR (Computed Tomography/ Magnetic Resonance) image fusion method for improved coupled dictionary learning on the basis of sparse representation
  • Brain CT/MR (Computed Tomography/ Magnetic Resonance) image fusion method for improved coupled dictionary learning on the basis of sparse representation

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Embodiment Construction

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0053] refer to figure 1 , the brain CT / MR image fusion method based on improved sparse representation coupling dictionary learning provided in the embodiment of the present invention, the method comprises the following steps:

[0054] Step 100, preprocessing stage: for the registered brain CT / MR source image I C , I R ∈ R MN , R MN Represents a vector space with M rows and N columns, using a sliding window with a step size of 1 to convert the source image...

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Abstract

The invention discloses a brain CT / MR (Computed Tomography / Magnetic Resonance) image fusion method for improved coupled dictionary learning on the basis of sparse representation, relates to the technical field of image processing, and can independently carry out fusion on three groups of brain medical images including a normal brain, encephalatrophy and brain tumors. Multiple experiment results indicate that an ICDL (Improved Coupled Dictionary Learning) method which is put forward by the invention improves the fusion quality of brain medical images, effectively lowers time for dictionary training and can provide effective help for clinic medical diagnosis if being compared with a method based on multi-scale transformation, a traditional sparse representation method, a method based on K-SVD (K-Singular Value Decomposition) dictionary learning and a multi-scale dictionary learning method.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a brain CT / MR image fusion method based on sparse representation-based improved coupling dictionary learning. Background technique [0002] In the medical field, doctors need to study and analyze a single image with both high spatial and hyperspectral information in order to accurately diagnose and treat diseases. This type of information cannot be obtained from single-modal images alone, for example, CT imaging can capture the bone structure of the human body with high resolution, while MR imaging can capture the details of the soft tissues of human organs such as muscle, cartilage, fat, etc. information. Therefore, fusing the complementary information of CT and MR images to obtain more comprehensive and rich image information can provide effective help for clinical diagnosis and auxiliary treatment. [0003] At present, the more classic methods applied in the field o...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0012G06T2207/10081G06T2207/10088G06T2207/20081G06T2207/20221G06T7/10
Inventor 王丽芳董侠成茜史超宇王雁丽
Owner ZHONGBEI UNIV
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