|Table of Contents|

Research on Image Preprocessing in Predicting the Bone Age ofHand Bone X-ray Films(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2021年02期
Page:
54-59
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research on Image Preprocessing in Predicting the Bone Age ofHand Bone X-ray Films
Author(s):
Su Ye; Li Jing; Xu Yinlin
School of Computer and Electronic Information,Nanjing Normal University,Nanjing 210023,China
Keywords:
bone age prediction; pretreatment; grayscale histogram equalization; U-Net network; deep learning
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2021.02.009
Abstract:
Problems of scale,artifact and noise in the X-ray pictures of hand bone always exist during the bone age prediction. The accuracy of prediction by normal filter and end-to-end deep learning neural network model is not so high normally. This paper uses the method of U-Net,which is used for biomedical image segmentation,to split hand bone X-ray film. The method uses image binarizaion to remove the background of mask generated by U-Net,and uses grayscale straight square equalization to solve the problem of brightness of images. Deep learning neural network prediction after fine pretreatments mentioned above can significantly improve the results of bone age prediction.

References:

[1] WILSON D M. Regular monitoring of bone age is not useful in children treated with growth hormone[J]. Pediatrics,1999,104(Suppl 5):1036-1039.
[2]黄陈力子. 中国儿童青少年乒乓球分龄赛运动员骨龄检测状况的研究[D]. 北京:北京体育大学,2015.
[3]陆慧玲,郑晶,顾学安,等. 青少年活体年龄推断的方法比较[J]. 中国法医学杂志,2002,17(增刊1):27-29.
[4]GREULICH W W,PYLE S I.Radiographic atlas of skeletal development of the hand and wrist[J]. The American Journal of the Medical Sciences,1959,238(3):393.
[5]TANNER J M,WHITEHOUSE R H,CAMERON N,et al. Assessment of skeletal maturity and prediction of adult height(TW 2 method)[M]. London:WB Saunders,2001:1-110.
[6]TANNER J M,HEALY M R J,GOLDSTEIN H,et al. Assessment of skeletal maturity and prediction of adult height(TW 3)[M]. London:WB Saunders,2001:243-254.
[7]KASHIF M,JONAS S,HAAK D,et al. Bone age assessment meets SIFT[C]//Medical Imaging 2015:Computer-Aided Diagnosis. Orlando,USA:International Society for Optics and Photonics,2015.
[8]王燚,钟俊航. 基于手部X光图像的骨龄检测系统设计与实现[J]. 信息系统工程,2015,32(1):28-30.
[9]李新华,赵娟,袁振宇,等. 基于k-余弦曲率和WSVM的骨龄识别方法[J]. 计算机应用与软件,2015,32(8):158-161.
[10]刘洁琳. 计算机辅助桡骨骨龄等级评估[D]. 北京:北京交通大学,2017.
[11]SPAMPINATO C,PALAZZO S,GIORDANO D,et al. Deep learning for automated skeletal bone age assessment in X-ray images[J]. Medical Image Analysis,2017,36:41-51.
[12]LEE H,TAJMIR S,LEE J,et al. Fully automated deep learning system for bone age assessment[J]. Journal of Digital Imaging,2017,30(4):427-441.
[13]MUTASA S,CHANG P D,RUZAL S C,et al. MABAL:a novel deep-learning architecture for machine-assisted bone age labeling[J]. Journal of Digital Imaging,2018,31(4):513-519.
[14]边增亚. 基于深度卷积神经网络的自动骨龄识别方法研究[D]. 北京:北京交通大学,2019.
[15]林珏伟. 基于深度学习的骨龄评估方法研究[D]. 杭州:浙江工业大学,2019.

Memo

Memo:
-
Last Update: 2021-06-30