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

Abstract

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.