Automatic segmentation method based on self-adaptive external force level set for magnetic resonance images (MRIs) of soft tissue and realization method thereof

A nuclear magnetic image, automatic segmentation technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as boundary leakage and inability to meet

Inactive Publication Date: 2012-09-19
SANITARY EQUIP INST ACAD OF MILITARY MEDICAL SCI PLA
View PDF3 Cites 25 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since this method only uses simple gradient information to control the evolution of the curve, boundary leakage often occurs when

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
  • Automatic segmentation method based on self-adaptive external force level set for magnetic resonance images (MRIs) of soft tissue and realization method thereof
  • Automatic segmentation method based on self-adaptive external force level set for magnetic resonance images (MRIs) of soft tissue and realization method thereof
  • Automatic segmentation method based on self-adaptive external force level set for magnetic resonance images (MRIs) of soft tissue and realization method thereof

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0054] The following describes in detail the automatic segmentation and realization method of the adaptive external force level set of the soft tissue nuclear magnetic image of the present invention in conjunction with the embodiments and the drawings.

[0055] The self-adaptive external force level set automatic segmentation method of soft tissue NMR images of the present invention is based on the opposite properties of the second-order differential signs on both sides of the zero-crossing point in the image domain, and uses the sign of the second-order differential value as the guiding term to drive the internal The evolution curve outside or intersecting with the target automatically and accurately approaches the edge of the target. At the same time, the model introduces a new boundary function, which can continuously adjust its parameters according to the characteristics of the image to be segmented to ensure that the evolution curve finally stays on the edge of the target acc...

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 provides an automatic segmentation method based on self-adaptive external force level set for magnetic resonance images (MRIs) of soft tissue and a realization method thereof. The automatic segmentation method comprises the steps that object region tissue satisfying a gray threshold is selected under the circumstance of interactive operation; gradient information of a smoothened image is calculated; a curve evolution guiding function is set; a boundary stopping function is set; and if an evolutional curve exceeds the boundary of an object region, parameters of the boundary stopping function are adjusted, then curve evolution is re-conducted and the iteration is conducted again until the evolutional curve is converged within the boundary of the object region. The automatic segmentation method has the characteristics of high real-time performance, high operation efficiency, capability of segmenting multiple discrete regions at the same time, capability of accurately recognizing fuzzy boundaries of the soft tissue of a human body, high segmentation precision, clear image detail characteristics, high intelligent degree, no need of manual intervention, stable and reliable operation, and the like.

Description

technical field [0001] The invention relates to a medical image segmentation method. In particular, it relates to a soft tissue nuclear magnetic image adaptive external force level set automatic segmentation and its implementation method, which has the characteristics of being able to automatically segment target tissues quickly and accurately without manual intervention. Background technique [0002] Image segmentation is one of the classic research topics in the fields of image processing, image analysis and computer vision, and it is also one of the difficulties. Provide basic support for quantitative and qualitative analysis. Due to the different methods of various images, especially medical images, certain random noise is inevitably introduced in the process, and the shape and intensity of the target of interest in the image are very different, so far there is no general method Segmentation suitable for all images. MRI (Nuclear Magnetic Resonance, Magnetic Resonance ...

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): G06T7/00
Inventor 魏高峰田丰孙秋明倪爱娟谢新武秦晓丽
Owner SANITARY EQUIP INST ACAD OF MILITARY MEDICAL SCI PLA
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
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap