Fuzzy clustering-based brain MR image segmentation method

An image segmentation and fuzzy clustering technology, applied in image analysis, image enhancement, image data processing, etc., to achieve the effect of bias field correction performance advantages, noise suppression, and anti-noise capabilities.

Active Publication Date: 2018-02-23
玛士撒拉无锡医疗科技有限公司
View PDF8 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above deficiencies, the present invention proposes a segmentation method of brain MR images based on fuzzy clustering, that is, a segmentation method of brain MR images based on an improved fuzzy C-mea

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
  • Fuzzy clustering-based brain MR image segmentation method
  • Fuzzy clustering-based brain MR image segmentation method
  • Fuzzy clustering-based brain MR image segmentation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0039] 1. Introduction to basic theory

[0040] 1. FCM algorithm

[0041] Consider a data set X={x consisting of n p-dimensional samples 1 ,x 2 ,...,x n}∈R n×p , the FCM algorithm aims at the objective function J FCM Minimize to realize the fuzzy division of the sample data, namely

[0042]

[0043] where U={u ki}∈R c×n is the membership matrix, satisfying V={v 1 ,v 2 ,...,v c} is the set of cluster centers, c∈[2,n] is the number of clusters, m∈[1,+∞) is the fuzzy index, m=2 is usually taken. Using the Lagrange multiplier method for J FCM Perform iterative update to minimize the objective function, we can get

[0044]

[0045]

[0046] Repeat formula (2) and formula (3) until the FCM algorithm converges.

[0047] 2.FLICM algorithm

[0048] The traditional FCM algorithm does not introduce spatial constraint information, and its segmentation results are not accurate enough. For this reason, Krinidis et al. proposed a Fuzzy Local Information C-Means (FLI...

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 invention discloses a fuzzy clustering-based brain MR image segmentation method. The invention mainly aims to solve the problem of the incapability of a traditional FCM (Fuzzy C-Means) algorithm and an improved method thereof to simultaneously eliminate noises and bias fields during a brain MR image segmentation process. According to the method of the invention, local spatial information, local grayscale information and non-local information in an image are fully utilized so as to construct a multi-local information fuzzy factor and a non-local weight, and the details of the image are preserved as much as possible while the anti-noise performance of the algorithm is improved; a bias field model is established to remove gray nonuniformity in the brain MR image; the multi-local information fuzzy factor and the non-local weight are embedded into an FCM method with the bias field model, so that the segmentation of the brain MR image under the noise and bias field condition can be realized. With the method of the invention adopted, noises in the brain MR image can be effectively suppressed, and the influence of the bias field on the segmentation of the brain MR image can be effectively eliminated. The method has better segmentation performance.

Description

technical field [0001] The invention belongs to the technical field of cluster analysis and intelligent information processing, and relates to the segmentation of brain MR images under noise and bias field environments. Specifically, it is a segmentation method of brain MR images based on fuzzy clustering, which can be used in the fields of medical image analysis and disease diagnosis. Background technique [0002] Magnetic Resonance Imaging (MRI), as a very important medical imaging technology, has become an indispensable technical means in medical diagnosis, surgical planning, 3D reconstruction, etc. A hot issue in the research. Brain tissue is usually divided into three parts: Gray Matter (GM), White Matter (WM), and Cerebro-Spinal Fluid (CSF). Accurate diagnosis is of great help. Due to the complexity of the brain tissue structure and the influence of some factors such as the partial volume effect in the medical imaging process, the acquired images often show characte...

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
IPC IPC(8): G06T7/11G06K9/62
CPCG06T7/11G06T2207/30016G06T2207/10088G06F18/23213
Inventor 葛洪伟陆海青葛阳
Owner 玛士撒拉无锡医疗科技有限公司
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