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Method for automatically identifying bone age image after digital processing

An automatic identification and image technology, applied in the field of bone age identification, can solve the problems of long time-consuming bone age reading and low accuracy, and achieve the effects of alleviating low reading speed, improving real-time performance, and alleviating low reading accuracy

Pending Publication Date: 2021-05-28
浙江康体汇科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems in the prior art, and propose a method for automatic identification of bone age images after digital processing, which solves the problem of time-consuming and low-precision reading of bone age images, and provides favorable support for the rapid assessment of bone age

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  • Method for automatically identifying bone age image after digital processing
  • Method for automatically identifying bone age image after digital processing
  • Method for automatically identifying bone age image after digital processing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] Embodiment 1, specific usage:

[0036] S-1. Image collection:

[0037] Import the captured hand bone image of the tester, the physiological data of the tester and the physiological data of the tester's parents.

[0038] Wherein, the hand bone image is a digitized left hand bone image taken by an X-ray bone density bone age measuring instrument.

[0039] Physiological data includes one or a combination of age, height, sex and weight.

[0040] When importing data, you can use one by one import method or batch import method. In addition, data can also be directly uploaded to the system by the bone age tester.

[0041] S-2. Image preprocessing (implemented using Python):

[0042] S-2-1. Image denoising processing:

[0043] Perform image denoising processing on the hand bone image imported in step S-1.

[0044] Wherein, a bilateral filtering algorithm (Bilateral Filters) is used to perform denoising processing on the image of the hand bone image. Bilateral filtering i...

Embodiment 2

[0067] Embodiment two, deep learning method:

[0068] 1. Hand bone image collection:

[0069] Collect hand bone images of different sexes and age groups from hospitals, clinics or health centers, etc. Wherein, the hand bone image is a digitized image taken by various devices, and the optical format includes DICOM, PNG, and JPG. At the same time, the image data of the left hand bone was captured by the dual-energy X-ray bone densitometry instrument SGY-Ⅱ. In addition, the later recognized images to be predicted are continuously added to the bone age sample data set in order to improve the accuracy of bone grade recognition, thereby improving the accuracy of bone age automatic recognition.

[0070] 2. Deep learning:

[0071] Adopt the same step S-2 of embodiment 1, carry out image denoising processing and image space transformation processing on the handbone image. Then use the same step S-3 as in Embodiment 1, and use the image labeling tool to mark the target bone area and...

Embodiment 3 test test 1

[0077] First, directly import the bone age image, and input the basic information of the tester (name: Li) and physiological data (gender: male; height: 168cm; weight: 34kg; age: 9 years old). Then, click the forecast button, and the system will automatically generate a report.

[0078] The content of the report is as follows:

[0079] Name: Li; Gender: Male; Height: 168cm; Weight: 34kg; Age: 9 years old; Tested bone age: 9.1; Future adult height: 180cm; Adult height of the same percentile: 176.7cm.

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Abstract

The invention discloses a method for automatically identifying a bone age image after digital processing, which comprises the steps of image collection, image preprocessing, target prediction and result reporting, and is characterized in that according to a bone development grade standard of a CHN method, 14 target bone areas of an image sample are respectively marked by using an image marking tool in the target prediction; a bone grade identification model trained through deep learning is adopted to position, segment and classify 14 target bone areas, then according to physiological data of a testee and according to a score table of bone development stages of a CHN method, bone grades of all the target bone areas are converted into different scores and accumulated, and the scores of all the target bone areas are obtained. And finally, the corresponding bone age is calculated according to the comparison table of the bone development maturing score and the bone age of the CHN method, so that the problems of long time consumption and low precision of bone age film reading are solved, and favorable support is provided for rapid evaluation of the bone age.

Description

【Technical field】 [0001] The present invention relates to the technical field of bone age identification, in particular to the technical field of an automatic identification method after digital processing of bone age images. 【Background technique】 [0002] Human growth and development can be represented by two "ages", namely life age (calendar age) and biological age (bone age). Bone age is the abbreviation of skeletal age, which is a state reflection of the bone growth degree of the human body, and needs to be determined by means of specific images produced by bones in X-ray photography. Bone age can more accurately reflect the level of growth and maturity of an individual. It not only helps to monitor the growth and development stage of the individual and diagnose certain diseases, but also can be used to predict the final height of the individual in adulthood and guide endocrine clinical medication. [0003] At present, in major hospitals, doctors generally obtain bone ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T5/00G06K9/46G06K9/62G06N3/08G06C3/00
CPCG06T7/0014G06N3/08G06C3/00G06T2207/10116G06T2207/20028G06T2207/20048G06T2207/30008G06V10/44G06V10/56G06F18/22G06T5/70
Inventor 李博毛科技杨威斌
Owner 浙江康体汇科技有限公司
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