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

Fuzzy SVM feedback measuring method used for target recognition of medical images

A medical image, fuzzy similarity technology, applied in the field of image processing, can solve the problems of sensitive selection, limited use, bad and so on

Inactive Publication Date: 2009-12-23
SOUTHERN MEDICAL UNIVERSITY
View PDF0 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the SVM algorithm is sensitive to the selection of positive and negative examples, and sometimes bad results will be obtained if the selection is not good.
The researchers used bagging and random sampling techniques to solve this problem. This method has requirements on the number of negative images and the dimensionality of feature vectors, which limits its use.

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 SVM feedback measuring method used for target recognition of medical images
  • Fuzzy SVM feedback measuring method used for target recognition of medical images
  • Fuzzy SVM feedback measuring method used for target recognition of medical images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] A fuzzy SVM feedback measurement method for medical image target recognition, such as figure 1 Shown include the following specific steps:

[0041] Step 1, the preprocessing step, reads medical images from the hospital PACS system, and uses Gaussian filtering to preprocess the input images to reduce the impact of noise on image processing, specifically through linear transformation (I-I min )×255 / (I max -I min ) transform the gray value of all pixels into the scope of 0~255, wherein, I is the image gray value, Imin is the minimum value of the image gray, and Imax is the maximum value of the image gray;

[0042] Step 1, performing medical image window width and window level adjustment and filtering on the medical image data in the feature database;

[0043] Step 2, extracting the hard features of the medical image processed in step 2, these hard features include grayscale histogram features, invariant rectangular shape features, Gabor texture features and direction h...

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 SVM feedback measuring method used for the target recognition of medical images, comprising the following steps: (1) regulating a window width and a window position of medical image data in a characteristic database and filtering; (2) extracting hard characteristics of the medical images processed by the step (1); (3) converting the hard characteristics extracted by the step (2) into the fuzzy characteristics which are stored into a characteristic database; (4) selecting one medical image to be compared and extracting the fuzzy characteristics of the medical image to be compared , and obtaining the fuzzy similarity of the medical image to be compared and medical images in the characteristic database, arraying the medical images in the characteristic database according to the value of the fuzzy similarity, and M images are output from high value to low value; (5) bringing the fuzzy characteristics of the M once output images into feedback treatment based on the fuzzy similarity to calculate, calculating the similarity of the medical images to be compared and all medical images in the characteristic database, and outputting N images from high value to low value sequentially. The feedback measuring method can effectively pick needed medical images.

Description

technical field [0001] The invention relates to an image processing method, in particular to a fuzzy SVM (Support Vector Machine, Support Vector Machine) feedback measurement method for medical image target recognition, which is suitable for medical image retrieval. Background technique [0002] With the increasingly wide application of medical digital imaging equipment in clinical practice, the technology of electronic medical records and picture archiving communication system (PACS) continues to develop, and a large amount of image data is generated every day in clinical practice (the image data of larger hospitals is more than 10G per day. many). How to effectively organize, manage and output medical images is an urgent problem to be solved. In clinical practice, in the diagnosis of undiagnosed clinical images and in the teaching browsing research, if the diagnosed image with the same content as the lesion image can be found through the output technology, the reliability...

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 江少锋冯衍秋陈武凡
Owner SOUTHERN MEDICAL UNIVERSITY
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