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A framework for automated bone age assessment from digital hand radiographs

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Abstract

Bone age assessment (BAA) is a method or technique that helps in predicting the age of a person whose age is unavailable and can also be used to find growth disorders if any. The automated bone age assessment system (ABAA) depends heavily on the efficiency of the feature extraction stage and the accuracy of a successive classification stage of the system. This paper has presented the implementation and analysis of feature extraction methods like Bag of features (BoF), Histogram of Oriented Gradients (HOG), and Texture Feature Analysis (TFA) methods on the segmented phalangeal region of interest (PROI) images and segmented radius-ulna region of interest (RUROI) images. Artificial Neural Networks (ANN) and Random Forest classifiers are used for evaluating classification problems. The experimental results obtained by BoF method for feature extraction along with Random Forest for classification have outperformed preceding techniques available in the literature. The mean error (ME) accomplished is 0.58 years and RMSE value of 0.77 years for PROI images and mean error of 0.53 years and RMSE of 0.72 years was achieved for RUROI images. Additionally results also proved that prior knowledge of gender of the person gives better results. The dataset contains radiographs of the left hand for an age range of 0-18 years.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Don Bosco College of Engineering, Fatorda, Goa, 403602, India

    Shreyas Simu

  2. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, 575025, India

    Shyam Lal

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  1. Shreyas Simu
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Simu, S., Lal, S. A framework for automated bone age assessment from digital hand radiographs. Multimed Tools Appl 79, 15747–15764 (2020). https://doi.org/10.1007/s11042-020-08816-7

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