# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
###### Abstract  
One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner-Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 \(\times\) 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different. Keywords:skeletal bone age prediction; regression network; image registration; X-ray image +
Footnote †: journal:  
successful athlete. One of the methods used by the pediatricians to assess the growth level of a child is by using skeletal bone age assessment [2]. This test is used to identify any growth disorder, in which the skeletal bones might be overdeveloped or underdeveloped with regards to the child chronological age. The primary cause of this disorder can be attributed to the lack of nutrition, genetics-related disease, or problem in hormonal discretions [3]. Generally, bone age assessment is diagnosed through a radiography X-ray image of the left-hand region, which is the non-dominant hand. X-ray is a powerful imaging modality where it is even used in astronomy-related observations [4]. The age differences can be observed through an X-ray image, especially regions with the bone growth plates, where they will become thinner as a child grows older and totally disappears once he becomes an adult. In early 1960, the most popular assessment is done through the Greulich and Pyle (G&P) method [5], which subjectively compares the captured hand X-ray with a set of hand atlas images. In general, the reliability of this method is low due to the high bias in inter and intra-observers, where clinical experience plays a crucial factor in reporting the right assessment. Rather than looking at the whole X-ray image, the method by [PERSON] (TW) [6] is looking at the specific regions on the X-ray image in order to reduce the assessment subjectivity [7].