A method for supervoxel generation of brain magnetic resonance images based on prior knowledge

A technology of magnetic resonance images and prior knowledge, applied in the field of supervoxel generation of brain magnetic resonance images based on prior knowledge, can solve the problems of MRI noise, weak boundary, lack of access, etc., and achieve a high degree of boundary fit , suppress the influence of noise, high efficiency

Active Publication Date: 2019-10-08
SOUTHEAST UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, for different types of images, current superpixel generation algorithms generally have mutual constraints between the number of superpixels, compactness, segmentation quality, and algorithm practicability.
At the same time, it is difficult to obtain better segmentation results for special targets.
Especially for the characteristics of MRI with large noise and weak boundaries, the above algorithm is not very suitable, especially when the image is seriously disturbed by noise, it cannot get a good superpixel generation effect

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for supervoxel generation of brain magnetic resonance images based on prior knowledge
  • A method for supervoxel generation of brain magnetic resonance images based on prior knowledge
  • A method for supervoxel generation of brain magnetic resonance images based on prior knowledge

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0053] The present invention provides a supervoxel generation method for brain magnetic resonance images based on prior knowledge. The probability distribution maps of three tissues are registered to individual templates. After selecting seed points, a new method is designed by integrating prior knowledge. Measure operator, which clusters voxels to seed points to generate supervoxels. The specific process of the present invention is as figure 1 shown, including the following steps:

[0054] Step 1, the probability map is registered to the individual space.

[0055] Registration is used to obtain the spatial transformation of the template image transformed into ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention disclosed a method based on the hyperplasm of the brain magnetic resonance image based on the priority knowledge. Based on the K‑Means cluster algorithm, the weighted by the space distance, pixel strength and priority knowledge is used as the final distance measurement.Perform clustering, divide the brain MRI image into a series of uniform and better fitting of the edge of the image.By incorporating the prior knowledge of different tissues of the brain, the present invention designs a new type of measurement operator and builds a rogue hyperin generating method to achieve the superfinxion of the hypercorcitic image of the brain magnetic resonance, which can reduce the image noise pairEffect of the segmentation results.Compared with the existing hyperpores, the method of the present invention is more efficient, the boundary fit is higher, and it can better suppress the impact of noise.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, and relates to a processing method of brain magnetic resonance images, and more specifically, relates to a supervoxel generation method of brain magnetic resonance images based on prior knowledge. Background technique [0002] Superpixel (superpixel) or supervoxel (supervoxel) is an image preprocessing technology that has developed rapidly in recent years. Compared with the basic unit in traditional processing methods—pixels, superpixels are more conducive to the extraction of local features and the expression of structural information, and can greatly reduce the computational complexity of subsequent processing. It has been obtained in the field of computer vision, especially in image segmentation. widely used. In addition, superpixels have also been introduced into other computer vision research fields such as object tracking, human pose estimation, object recognition,...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/11G06K9/62G06T7/33
CPCG06T2207/20156G06T2207/30016G06T2207/10088G06F18/23213G06F18/22
Inventor 孔佑勇任洲甫左雨林沈傲东伍家松舒华忠
Owner SOUTHEAST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap