Spine feature point automatic recognizing method based on average curvature flow

An average curvature flow and automatic recognition technology, applied in the field of medical image processing, can solve the problems of manual selection of feature points, inaccurate feature point labeling, and low data accuracy, so as to avoid deviation, be easy to implement, and improve accuracy. Effect

Inactive Publication Date: 2016-12-14
NORTHWESTERN POLYTECHNICAL UNIV
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AI Technical Summary

Problems solved by technology

Using a purely manual method to label feature points, the feature point labeling is inaccurate, resulting in low accuracy of measured data and inaccurate model matching
The method of using Gaussian curvatur

Method used

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  • Spine feature point automatic recognizing method based on average curvature flow
  • Spine feature point automatic recognizing method based on average curvature flow
  • Spine feature point automatic recognizing method based on average curvature flow

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Embodiment Construction

[0047] The present invention will be described in further detail below in conjunction with the examples.

[0048] see figure 1 , an automatic identification method of spine feature points based on average curvature flow, including the following steps:

[0049] In step 1, a three-dimensional reconstruction is performed on the CT image of the human spine to obtain a three-dimensional model of the vertebral body with the required feature points marked.

[0050] see figure 2 , image 3 , Figure 4 , perform enhancement and threshold extraction on the spine CT data to segment the range of the target vertebral body. The segmentation of the vertebral body can be performed using the ScanIP module of the simpleware software, such as Figure 5 , Image 6 shown. In the process of model building, image enhancement, image threshold segmentation and region growing algorithm, denoising and other steps can be used to ensure the accuracy of the model.

[0051] In step 2, meshing is pe...

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Abstract

A spine feature point automatic recognizing method based on average curvature flow includes the steps of firstly, conducting three-dimensional reconstructing on a CT image of a human spine to obtain a three-dimensional spine model of to-be-marked feature points; secondly, conducting meshing on the spine model obtained in the first step; thirdly, manually selecting a certain point on the model, and calculating the average curvature value of all vertexes in the spherical space, with infinitesimal radius R, around the manually-selected point; finally, selecting n points, with the maximum average curvature, around the manually-selected point, and obtaining inner products between the n points with the maximum average curvature and the selected point respectively to obtain the point with the minimum included angle. The marking precision of the feature points can be improved, and therefore data measurement precision is improved, and the method has the advantages of being high in precision and easy to implement.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to an automatic identification method of spine feature points based on average curvature flow. Background technique [0002] The spine is an important supporting bone for the human body. In recent years, with the changes in people's living and working styles, the incidence of spinal diseases is increasing. Due to the complexity of the vertebral structure and the characteristics of the spine surgery itself, the current spine surgery is very difficult and dangerous. The precise positioning and registration of the spine model is still an unsolved problem in diagnosis and treatment. [0003] In the localization and registration of the spine model, the main task is the localization of feature points. Using a purely manual method to label feature points, the labeling of feature points is inaccurate, resulting in inaccurate measured data and inaccurate model ...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10081G06T2207/30012
Inventor 惠宇武君胜鱼滨李伟刚杨文超杜静李航
Owner NORTHWESTERN POLYTECHNICAL UNIV
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