A brain tissue segmentation method based on regularized graph segmentation

A brain tissue and tissue technology, applied in the field of brain tissue segmentation and brain magnetic resonance image processing based on regularized graph cuts, can solve problems such as high computational complexity, achieve high efficiency, fast processing speed, and high boundary fit Effect

Active Publication Date: 2019-01-29
SOUTHEAST UNIV
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Problems solved by technology

The voxel segmentation method can achieve excellent performance, but in MRI, when facing tens of thousands of supervoxels, it will encounter high computational complexity, which limits its application in MRI

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  • A brain tissue segmentation method based on regularized graph segmentation
  • A brain tissue segmentation method based on regularized graph segmentation
  • A brain tissue segmentation method based on regularized graph segmentation

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Embodiment Construction

[0047] The technical solutions provided by the present invention will be described in detail below in conjunction with specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and not to limit the scope of the present invention.

[0048] The present invention provides a brain tissue segmentation method based on regularized graph cutting. The process is mainly divided into two parts: First, the supervoxel generation method based on intensity distance and spatial similarity is used to divide voxels into regular ones. Reduce the influence of noise and better fit the supervoxels at the edge of the image; then, combined with the prior knowledge of brain tissue, use graph cuts to cut each supervoxel into specific brain tissue. The specific process of the present invention is as figure 1 As shown, including the following steps:

[0049] Step 1: Generation of supervoxels:

[0050] The present invention uses a su...

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Abstract

The invention discloses a brain tissue segmentation method based on regularized graph segmentation . Firstly, a new calculation method of similarity between voxels is designed based on intensity distance and spatial similarity, so as to cluster voxels, and the brain MRI image is divided into a series of supervoxels which are uniform and well fitting to the image edges. By incorporating a priori probabilities of different brain tissues into the graph cutting framework, a formula of energy calculation is designed to calculate the energy value of each supervoxel when each supervoxel is assigned different labels. Then the supervoxel is segmented by graph cutting method and the Magnetic Resonance Imaging (MRI) image is segmented into different tissues. The invention can segment three kinds of brain tissues from the initial brain MRI, and the boundary fitting degree between each tissue in the segmentation result is high. Compared with the prior MRI image segmentation method, the invention has better segmentation effect, higher boundary fitting degree, higher efficiency and faster processing speed, and can better suppress the influence of noise.

Description

Technical field [0001] The invention belongs to the technical field of digital image processing, and relates to a brain magnetic resonance image processing method, and more specifically, to a brain tissue segmentation method based on regularized graph cutting. Background technique [0002] Magnetic resonance imaging (MRI) has been widely used to examine the anatomical structure of the human brain in clinical applications and neuroscience research. Compared with other medical imaging methods, MRI has the advantages of high spatial resolution and good soft tissue contrast, and can finely distinguish different types of tissues. Precise segmentation of these tissues is essential for some applications. [0003] In order to facilitate the processing of MRI images, the concept of supervoxels is introduced. Supervoxel or superpixel is an image preprocessing technology that has been rapidly developed in recent years. It refers to the partial, consistent sub-regions in the image that can m...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/162G06T7/11
CPCG06T2207/10088G06T2207/20072G06T2207/30016G06T7/11G06T7/162
Inventor 章品正李艺飞孔佑勇伍家松杨淳沨舒华忠
Owner SOUTHEAST UNIV
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