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

Medical image efficient classification management method based on big data

A medical image and classification management technology, applied in the field of medical images, can solve the problem of medical images reducing the work efficiency of doctors, and achieve the effects of fast calculation speed, improved classification accuracy, and obvious texture

Inactive Publication Date: 2019-11-01
SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
View PDF4 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Failure to efficiently manage medical images greatly reduces physician productivity

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 efficient classification management method based on big data
  • Medical image efficient classification management method based on big data
  • Medical image efficient classification management method based on big data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0035] Taking the brain tumor image as an example, after obtaining the brain MRI image set, since the lesion in the T2-weighted sequence image is brighter than other healthy brain tissues, the T2-weighted image is first screened out and the image is standardized, and the image The size is unified to 256*256, and the background is removed by the threshold method, such as figure 2 , where (a) is the image before background removal, and (b) is the image after background removal. Afterwards, Gaussian filtering is performed on the image. Gaussian filtering is a relatively common noise removal algorithm. The main steps are to select a Gaussian template and convolve the Gaussian template with the image. In this method, a 5*5 Gaussian template is used.

[0036] Because the original image has a low contrast, it needs to be enhanced by an image enhancement algorithm. This method adopts the method of superimposing two image enhancement methods to obtain a better effect than a single ima...

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 efficient classification management method based on big data, and the method comprises the steps: carrying out the image standardization of a T2 weighted magnetic resonance image, and obtaining a standardized image; performing gaussian filtering on the standardized image; performing contrast stretching on the denoised image; extracting brightness features of the stretched image; carrying out histogram equalization on the stretched image to obtain an image with enhanced contrast; extracting texture features of the contrast-enhanced image through a gray level co-occurrence matrix; training and verifying the classification model through a support vector machine by adopting a one-leaving method; establishing a graphical user interface for human-computerinteraction. According to the invention, the classification precision of the classifier is improved and the time complexity of the algorithm is reduced through background removal; through two times of image enhancement, the texture of the image is more obvious, so that the classification precision is improved; the working efficiency of radiologists can be improved; different features are extracted for different stages, and image features of different stages are met.

Description

technical field [0001] The invention relates to the field of medical images, in particular to an efficient classification management method for medical images based on big data. Background technique [0002] Traditional medical image management is to manually classify the medical images of patients according to different medical image sequences, and does not classify whether the medical images are abnormal. In actual situations, each patient has hundreds of images, and there is a large shortage of radiologists and insufficient personnel. Doctors in large hospitals have to make decisions on tens of thousands of images every day, and the workload is huge. In addition, when the patient is re-examined, the doctor needs to recall the patient's medical images and screen out the abnormal parts. Failure to efficiently manage medical images greatly reduces physician productivity. Contents of the invention [0003] Aiming at the deficiencies of the prior art, the present invention...

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): G06K9/62G06K9/36G06K9/46
CPCG06V10/20G06V10/40G06V10/56G06F18/40G06F18/2411
Inventor 赵亿文黄钲宋国立赵新刚韩建达
Owner SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
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