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Automatic feature extraction in X-ray image based on deep learning approach for determination of bone age

机译：基于深度学习方法测定骨龄的深度学习方法的自动特征提取

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摘要

Aim: Determination of bone age is an important method to skeletal maturity and growth potential. This paper proposes a determination bone age via X-ray image recognition to obtain a better identification effect by comparison with state-of-the-art techniques. Methods : The proposed approach comprises two steps: the feature extraction and classification method. The feature extraction utilizes depth neural network to study the features of X-ray image, and the Local Binary Patterns (LBP) features and Glutamate cysteine ligase modifier subunit (GCLM) features in the image are extracted. Then, the classification method base on support vector machine is used to classify the features. Results: The experimental results show that the average absolute error of bone age assessment model based on multi-dimensional data feature fusion is 0.455, which is superior to the traditional method and support vector machine method. Because the model is based on feature extraction of deep neural network, it shows that the feature extraction method based on deep neural network can extract feature information better than traditional image analysis method. Conclusion: Compared with the traditional feature extraction method, the feature extraction based on deep convolution neural network has better performance in the bone age regression model. Combining population and gender information, the accuracy of bone age prediction based on image can be further improved.
机译：目的：骨龄的测定是骨骼成熟度和生长潜力的重要方法。本文提出了通过X射线图像识别的确定骨龄，以通过与最先进的技术进行比较来获得更好的识别效果。方法：所提出的方法包括两个步骤：特征提取和分类方法。该特征提取利用深度神经网络来研究X射线图像的特征，并提取图像中的局部二进制图案（LBP）特征和谷氨酸半胱氨酸连接酶改性亚基（GCLM）特征。然后，用于支持向量机上的分类方法用于对特征进行分类。结果：实验结果表明，基于多维数据特征融合的骨龄评估模型的平均绝对误差为0.455，优于传统方法和支持向量机方法。因为该模型基于深度神经网络的特征提取，所以表明基于深神经网络的特征提取方法可以提取比传统图像分析方法更好的特征信息。结论：与传统的特征提取方法相比，基于深度卷积神经网络的特征提取在骨龄回归模型中具有更好的性能。组合人口和性别信息，可以进一步改善基于图像的骨龄预测的准确性。

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  • 来源
    《Future generation computer systems》 |2020年第9期|795-801|共7页
  • 作者

    Xu Chen; Jianjun Li; Yanchao Zhang; Yu Lu; Shaoyu Liu;

  • 作者单位

    Department of Orthopedic Surgery The Seventh Affiliated Hospital Sun Yat-sen University Shenzhen Guangdong 518107 China;

    Department of Orthopedic Surgery Shengjing Hospital of China Medical University Shenyang Liaoning 110004 China;

    Department of Orthopedic Surgery Shengjing Hospital of China Medical University Shenyang Liaoning 110004 China;

    College of Big Data and Internet Shenzhen Technology University Shenzhen Guangdong 518118 China;

    Department of Orthopedic Surgery The Seventh Affiliated Hospital Sun Yat-sen University Shenzhen Guangdong 518107 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bone age; Deep learning; Medical imaging; Feature extraction; Classification method;

    机译：骨骼年龄;深度学习;医学影像;特征提取;分类方法;

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