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A brain MRI image segmentation method based on fuzzy multi-threshold and region information

An image segmentation and regional information technology, which is applied to the segmentation of brain MRI images based on fuzzy multi-threshold and regional information, and the field of fuzzy multi-threshold segmentation.

Active Publication Date: 2019-03-29
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Its disadvantage is that it only considers the influence of a single pixel on the segmentation result, and does not use the regional characteristics of the image, and the segmentation result is prone to isolated pixels and regions.
In addition, the traditional threshold segmentation method is not suitable for the gray level inhomogeneity and medical artifacts of brain MRI images.

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  • A brain MRI image segmentation method based on fuzzy multi-threshold and region information
  • A brain MRI image segmentation method based on fuzzy multi-threshold and region information
  • A brain MRI image segmentation method based on fuzzy multi-threshold and region information

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Embodiment Construction

[0065] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0066] The invention is a brain MRI image segmentation method based on fuzzy multi-threshold and region information. By combining the ideas of threshold segmentation and regional information aggregation, the traditional medical image segmentation algorithm is improved, and the segmentation quality of brain MRI images is effectively improved.

[0067] The invention is based on fuzzy multi-threshold image segmentation. Assume that in the segmentation of image I, whether a pixel i is classified into region R is represented by set P.

[0068] P={(i, μ R (i))|i∈I},0≤μ R (i)≤1,R∈[1,m]

[0069] Among them, m is the number of divided regions. mu R (i) is called the membership function of pixel i belonging to region R, which is used to measure the close relationship between pixel i and region R. The closer the relationship is, the clos...

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Abstract

Aiming at the influence of the gray unevenness of the brain MRI image and the medical artifact on the segmentation in the prior art, the invention adopts the combination of the region information aggregation and the threshold segmentation as the design idea, introduces the fuzzy multi-threshold technology, and puts forward a brain MRI image segmentation method based on the fuzzy multi-threshold and the region information. Based on fuzzy multi-threshold segmentation, By constructing fuzzy membership function and fuzzy membership aggregation based on local information, To further improve the quality of image segmentation; the invention adopts the fuzzy theory and the information aggregation technology, suppresses the influence of the gray level non-uniformity and the medical artifact on thesegmentation result, can retain more original image information, effectively avoids the false segmentation caused by the artifact, and improves the effect of brain MRI image segmentation. The invention adopts an improved quantum particle swarm optimization algorithm and introduces an exponential descent type shrinkage expansion coefficient. the search performance of the algorithm is improved. At the same time, the convergence rate of the algorithm is improved.

Description

technical field [0001] The invention belongs to the technical field of image segmentation, relates to fuzzy multi-threshold segmentation technology, and mainly relates to a brain MRI image segmentation method based on fuzzy multi-threshold and region information. Background technique [0002] Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. It is a key step from image processing to image analysis. Compared with natural image segmentation, medical image segmentation is often a more complex problem depending on specific applications, imaging modalities, and specific body parts. Many algorithms that work well in natural image segmentation have problems such as under-segmentation and over-segmentation in medical image segmentation, so they cannot be transplanted well. For example, in brain magnetic resonance imaging (Magnetic Resonance Imaging, MRI) images, its typical ...

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Application Information

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IPC IPC(8): G06T7/11G06T7/136G06T5/00G06N3/00
CPCG06N3/006G06T7/11G06T7/136G06T2207/10088G06T2207/30016G06T5/70
Inventor 郭剑刘峰宁韩崇肖甫王娟孙力娟
Owner NANJING UNIV OF POSTS & TELECOMM
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