Bone age evaluation method based on non-subsampled contour wave transform and convolution neural network

A non-subsampling contour and convolutional neural network technology, applied in the field of medical image analysis, can solve difficult problems and achieve the effect of improving quality, separation and performance

Active Publication Date: 2019-01-01
HEFEI UNIV OF TECH
View PDF5 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the problems existing in the prior art, the present invention provides a bone age assessment method based on non-subsampling contourlet transform and convolutional neural network, in order to solve the problem of network a

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
  • Bone age evaluation method based on non-subsampled contour wave transform and convolution neural network
  • Bone age evaluation method based on non-subsampled contour wave transform and convolution neural network
  • Bone age evaluation method based on non-subsampled contour wave transform and convolution neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] figure 1 It is a flowchart of a bone age assessment method based on non-downsampled contourlet transform and convolutional neural network of the present invention, which includes the following steps:

[0032] Step 1: Take the hand X-ray image as the original input image and perform spatial size processing of interpolation and boundary continuous extension to obtain a normalized hand X-ray image;

[0033] In this embodiment, take a bone age image in the public bone age assessment data set Digital HandAtlas provided by the University of Southern California as an example. figure 2 As shown, the subject is an Asian girl whose actual age is 10.49 years old, and the original spatial resolution of the image is 1526*1893 pixels. Since the subsequent convolutional network requires a fixed spatial resolution of the input image, and considering the computational efficiency and storage cost, it is necessary to normalize the size of the original input image. Here, the original input im...

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 an X-ray image bone age evaluation method based on non-downsampled contour wave transform and convolution neural network, includes such steps as non-downsampled contour wave transformation of normalized size X-ray image, The high-frequency directional subbands and a low-frequency coefficient map at several scales are obtained, and then they are input into a multi-channel convolutional neural network to obtain the characteristic maps at different scales. Finally, these characteristic maps are stacked together and input into a regression network composed of several connected layers to obtain the bone age prediction values. The above process is implemented in an end-to-end network structure, and error back propagation mechanism is used to achieve network training. Themethod of the invention utilizes the non-down sampling contour wave transform to pre-extract and separate the features of the original spatial domain image, can overcome the difficulty of network training of the existing depth learning method on the small-scale data set, improve the generalization performance of the network, and thus provides a more accurate and reliable evaluation method for clinical application.

Description

Technical field [0001] The invention relates to the field of medical image analysis, in particular to a bone age assessment method based on non-down-sampling contourlet transform and convolutional neural network. Background technique [0002] As a commonly used technique in pediatric radiology, bone age assessment is mainly used to judge the maturity of children's skeletal development. It is of great significance to the diagnosis of endocrine disorders, the monitoring of growth hormone therapy, and the prediction of the final height of adolescents. At present, the most popular clinical bone age assessment method is based on the X-ray images of the subject's left hand and wrist. The doctor completes the bone age assessment of the subject by analyzing the development of the corresponding bone structure in the X-ray image. However, the accuracy of bone age assessment results depends heavily on the doctor's experience and level. The assessment results of different doctors may vary gr...

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): G06T7/00G06N3/04
CPCG06T7/0012G06T2207/30008G06T2207/10081G06N3/045
Inventor 刘羽张超陈勋成娟李畅宋仁成
Owner HEFEI UNIV OF TECH
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