Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Medical image clustering method

A clustering method, medical image technology, applied in the field of image processing

Inactive Publication Date: 2013-10-02
JIANGSU UNIV
View PDF1 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to propose a medical image clustering method that fully considers the characteristics of neighborhood information and interval properties in view of the limitations of the above-mentioned prior art, so as to effectively solve the problem of medical image noise data skewness and noise-free The symmetry contradiction of medical image data can significantly improve the anti-noise ability, make the clustered image closer to the objective and real situation of each object, and provide more accurate image basis for medical diagnosis

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
  • Medical image clustering method
  • Medical image clustering method
  • Medical image clustering method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0039] The medical image clustering method will be further described below mainly in conjunction with the accompanying drawings and specific embodiments.

[0040] The present embodiment selects the brain MRI figure to analyze, and selects the original brain MRI figure 3a containing noise points to illustrate the corresponding results after the implementation of the present invention. The specific steps are as follows:

[0041] A. The computer reads the original image of the brain MRI map, and obtains the interval value of the image pixel point through the relationship between the image pixel point and its surrounding 8 neighborhood information, and uses the interval value Each pixel of the image is represented by .

[0042] B. According to the rough set principle, the left interval of each pixel interval value x N and the right interval Approximate into upper approximation and lower approximation, calculate the roughness ρ of each interval value A (x k ).

[0043] C. D...

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 relates to a medical image clustering method, and belongs to the field of image processing. The method is based on a downhill algorithm, and comprises the following steps of preprocessing an image, extracting an image characteristic, searching a density attractor, searching hill foot pixels, and separating a layout image. Theories and practices prove that the method achieves a ridge, a flat top and a single threshold appropriately, and after the method is adopted, the medical image can be clustered efficiently, and the image characteristic is not lost, misled or omitted, so that the high-quality medical clustering image can be obtained, and diagnosis and reading requirements of medical workers are met.

Description

technical field [0001] The invention relates to an image clustering method, in particular to a medical image clustering method, which belongs to the field of image processing. Background technique [0002] With the rapid development of human medical imaging technologies such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), medical images are playing an increasingly important role in clinical medical diagnosis. [0003] Cluster analysis is one of the main techniques of data mining, and it is an unsupervised learning process. According to some attributes of things, it gathers things into classes, so that the similarity within a class is as small as possible, and the similarity between classes is as large as possible. Cluster analysis has been widely used in many fields, including pattern recognition, data analysis, image processing, and market research. Using cluster analysis to classify target objects in a single image is called image clustering. Image clu...

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 Applications(China)
IPC IPC(8): G06F17/30
Inventor 刘哲宋余庆刘毅刘雅婧
Owner JIANGSU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com