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

Optic Cup Segmentation Method and Imaging Method Based on Rich Context Network

A context and video disk technology, applied in the field of image processing, can solve the problems of low utilization rate of context information, unclear boundaries, difficult to obtain segmentation results, etc., achieve good segmentation effect, improve segmentation performance, and solve the effect of not being smooth enough

Active Publication Date: 2022-05-10
CENT SOUTH UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing deep learning methods still do not have a high utilization rate of context information. Therefore, it is still difficult to obtain more accurate segmentation results for the visual cup with unclear boundaries.

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
  • Optic Cup Segmentation Method and Imaging Method Based on Rich Context Network
  • Optic Cup Segmentation Method and Imaging Method Based on Rich Context Network
  • Optic Cup Segmentation Method and Imaging Method Based on Rich Context Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] Such as figure 2 Shown is a schematic flow chart of the method for segmenting the optic cup and optic disc of the present invention: the method for segmenting the optic cup and optic disc based on the rich context network provided by the present invention includes the following steps:

[0049] S1. Obtain existing color fundus image data;

[0050] S2. Process the color fundus image data obtained in step S1 to obtain a training data set;

[0051] During specific implementation, the processing specifically includes performing random mirror flip and scale scaling on the color fundus image, and at the same time cutting the color fundus image with the optic disc as the center to obtain the complete optic disc area and a set size (preferably 400×400 to 800×800 pixel interval) window image;

[0052] S3. Construct the original model of optic cup and disc segmentation (structure such as image 3 shown); specifically, the following steps are used to construct the original mode...

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 method for segmenting the optic cup and optic disc based on a rich context network, which includes acquiring existing color fundus image data and processing to obtain a training data set; constructing an original model for optic cup and optic disc segmentation and training to obtain an optic cup and optic disc segmentation model; adopting The cup-optic-disc segmentation model segmented the target color fundus image to obtain the final cup-optic-disc segmentation result. The invention also discloses an imaging method using the optic cup and optic disc segmentation method based on the rich context network. The present invention proposes a segmentation structure based on a convolutional neural network and capable of obtaining sufficient context information for optic disc and cup segmentation; therefore, the method of the present invention can improve the segmentation performance of the optic disc and cup, and solve the problem that the segmentation of the optic cup edge is not smooth enough, Moreover, the accuracy is high, the reliability is good, and the segmentation effect is good.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to an optic cup and disc segmentation method and an imaging method based on a rich context network. Background technique [0002] With the development of economy and technology and the improvement of people's living standards, people pay more and more attention to health. [0003] Fundus images are an important part of clinical medical images. In fundus images, commonly used indicators include cup-to-disk ratio (the ratio of the radius of the optic cup in the vertical direction to the optic disc), disc radius, and the area ratio of the disc along the optic disc. The prerequisite for obtaining the above indicators is to segment the optic cup and optic disc in the fundus image, so as to obtain a more referential image of the optic cup and optic disc. [0004] In color fundus images, the optic disc is a bright yellow oval structure consisting of the optic cup and retinal n...

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 Patents(China)
IPC IPC(8): G06T7/12G06T7/11G06T7/00G06V10/44G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06T7/12G06T7/11G06T7/0012G06N3/084G06T2207/10024G06T2207/20081G06T2207/30041G06V10/44G06N3/048G06N3/045G06F18/2415
Inventor 陈再良颜丽沈海澜
Owner CENT SOUTH 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