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Bone Age Assessment in Young Children Using Automatic Carpal Bone Feature Extraction and Support Vector Regression

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Abstract

Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it to assess bone age in young children. Our proposed technique can detect the boundaries of carpal bones in X-ray images by using the information from the vector image model and the edge map. Feature analysis of the carpal bones can reveal the important information for bone age assessment. Five features for bone age assessment are calculated from the boundary extraction result of each carpal bone. All features are taken as input into the support vector regression (SVR) that assesses the bone age. We compare the SVR with the neural network regression (NNR). We use 180 images of carpal bone from a digital hand atlas to assess the bone age of young children from 0 to 6 years old. Leave-one-out cross validation is used for testing the efficiency of the techniques. The opinions of the skilled radiologists provided in the atlas are used as the ground truth in bone age assessment. The SVR is able to provide more accurate bone age assessment results than the NNR. The experimental results from SVR are very close to the bone age assessment by skilled radiologists.

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Acknowledgements

The authors would like to thank the Office of the Higher Education Commission, Thailand, for supporting by grant fund under the Strategic Scholarships for Frontier Research Network for the Ph.D. Program. We would like to thank Dr. Wichai Kultangwattana from the Medical School, Chiang Mai University, and Dr. Pimpaporn Pattarakittitada from the Nongbualamphu Hospital for the segmentation ground truth of the carpal bone images used in this research.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Krit Somkantha & Nipon Theera-Umpon

  2. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Sansanee Auephanwiriyakul

  3. Biomedical Engineering Program, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Nipon Theera-Umpon & Sansanee Auephanwiriyakul

Authors
  1. Krit Somkantha
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  2. Nipon Theera-Umpon
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  3. Sansanee Auephanwiriyakul
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Correspondence to Nipon Theera-Umpon.

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Somkantha, K., Theera-Umpon, N. & Auephanwiriyakul, S. Bone Age Assessment in Young Children Using Automatic Carpal Bone Feature Extraction and Support Vector Regression. J Digit Imaging 24, 1044–1058 (2011). https://doi.org/10.1007/s10278-011-9372-3

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  • DOI: https://doi.org/10.1007/s10278-011-9372-3

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