Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A CT medical image data enhancement method for cholelithiasis based on depth learning

A medical image and deep learning technology, applied in the field of image processing, can solve problems such as enhanced processing methods for CT medical image data of undiscovered cholelithiasis, achieve good visual effects and medical effects, improve fitting ability, and improve reconstruction ability

Inactive Publication Date: 2019-01-29
CHINA UNIV OF PETROLEUM (EAST CHINA)
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, no data enhancement processing method for CT medical images of cholelithiasis has been found in the market

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 CT medical image data enhancement method for cholelithiasis based on depth learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] The present invention will be further described below in conjunction with specific embodiment

[0031] Such as figure 1 As shown, the cholelithiasis CT medical image data enhancement method based on deep learning described in the present embodiment, its specific circumstances:

[0032] 1) Select 90 CT medical images of cholelithiasis as the initial training set of convolutional neural network based on deep learning-based CT medical image data enhancement of cholelithiasis, and then improve the training set. The specific steps are as follows:

[0033] 1-1) Using the set segmentation threshold, segment each CT medical image with gallstone lesions, and each image will be segmented into four image blocks of different sizes.

[0034] 1-2) Randomly adopt any one or more of the following steps to expand the above data set:

[0035] A step of enlarging or reducing images in the training set;

[0036] performing a rotation step on the images in said training set;

[0037] pe...

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

A CT medical image data enhancement method for cholelithiasis based on depth learning is provided. The method includes constructing CT medical image data enhancement convolution neural network for cholelithiasis. The network consists of four convolution units. Firstly, CT medical image dataset of cholelithiasis is constructed as the input of neural network, and the image is enhanced by edge information and redundant information is removed. Then, the image is cut according to the segmentation threshold to form many image blocks, and the dataset is expanded by scaling, rotation and translation.Convolution neural network (CNN) is trained continuously with data set, and adaptive lifting neural network (ALN) is used to extract image features, stretch contrast, equalize histogram and reconstruct image. Convolution neural network model can be used to enhance CT medical images of cholelithiasis. The method can realize real-time enhancement of cholelithiasis CT medical image data, and achievegood visual and medical effects.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for enhancing CT medical image data of cholelithiasis based on deep learning. Background technique [0002] The process of image recognition includes preprocessing, feature extraction, feature matching, similarity calculation and other links. An important link in preprocessing is image enhancement processing, which aims to enhance the useful information in the image and improve the visual effect of the image, aiming at the application occasion of the given image. Image data enhancement is a very important part, and its processing effect directly affects the subsequent image recognition process. [0003] The cholelithiasis involved in the present invention is a common digestive system disease, which has various disease types, complex pathogenic factors, high incidence rate, difficulty in dissolving and expulsing stones, and the like. In addition, there are various types ...

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): G06T5/00G06T7/11G06T7/136G06T3/40G06T3/60
CPCG06T5/00G06T3/4046G06T3/60G06T7/11G06T7/136G06T2207/10081G06T2207/20081G06T2207/20084
Inventor 王硕宋弢王珣丁桐孟凡
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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
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