A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features
Accurate assessment of skeletal maturity is important clinically. Skeletal age assessment is usually based on features encoded in ossification centers. Therefore, it is critical to design a mechanism to capture as much as possible characteristics of features. We have observed that given a feature, t...
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Hindawi Limited
2018
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oai:localhost:DHQB_123456789-38022018-10-22T08:44:14Z A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features Journal of Biomedicine and Biotechnology Dong Hua (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA) Fang Liu (Department of Computer Science, University of Texas-Pan American, 1201 W. University Drive, Edinburg, TX 78539, USA) Abdou Youssef (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) Technology Chemical technology Biotechnology Accurate assessment of skeletal maturity is important clinically. Skeletal age assessment is usually based on features encoded in ossification centers. Therefore, it is critical to design a mechanism to capture as much as possible characteristics of features. We have observed that given a feature, there exist stages of the skeletal age such that the variation pattern of the feature differs in these stages. Based on this observation, we propose a Bayesian cut fitting to describe features in response to the skeletal age. With our approach, appropriate positions for stage separation are determined automatically by a Bayesian approach, and a model is used to fit the variation of a feature within each stage. Our experimental results show that the proposed method surpasses the traditional fitting using only one line or one curve not only in the efficiency and accuracy of fitting but also in global and local feature characterization. 2018-08-22T03:53:46Z 2018-08-22T03:53:46Z 2009 Other http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/3802 en_US Hindawi Limited |
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Trung tâm Học liệu Đại học Quảng Bình (Dspace) |
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Trung tâm Học liệu Đại học Quảng Bình (Dspace) |
| language |
en_US |
| topic |
Technology Chemical technology Biotechnology |
| spellingShingle |
Technology Chemical technology Biotechnology Dong Hua (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA) Fang Liu (Department of Computer Science, University of Texas-Pan American, 1201 W. University Drive, Edinburg, TX 78539, USA) Abdou Youssef (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features |
| description |
Accurate assessment of skeletal maturity is important clinically. Skeletal age assessment is usually based on features encoded in ossification centers. Therefore, it is critical to design a mechanism to capture as much as possible characteristics of features. We have observed that given a feature, there exist stages of the skeletal age such that the variation pattern of the feature differs in these stages. Based on this observation, we propose a Bayesian cut fitting to describe features in response to the skeletal age. With our approach, appropriate positions for stage separation are determined automatically by a Bayesian approach, and a model is used to fit the variation of a feature within each stage. Our experimental results show that the proposed method surpasses the traditional fitting using only one line or one curve not only in the efficiency and accuracy of fitting but also in global and local feature characterization. |
| format |
Other |
| author |
Dong Hua (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA) Fang Liu (Department of Computer Science, University of Texas-Pan American, 1201 W. University Drive, Edinburg, TX 78539, USA) Abdou Youssef (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) |
| author_facet |
Dong Hua (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA) Fang Liu (Department of Computer Science, University of Texas-Pan American, 1201 W. University Drive, Edinburg, TX 78539, USA) Abdou Youssef (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) |
| author_sort |
Dong Hua (Department of Computer Science, The George Washington University, 801 22nd Street NW, Washington, DC 20052, USA) |
| title |
A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features |
| title_short |
A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features |
| title_full |
A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features |
| title_fullStr |
A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features |
| title_full_unstemmed |
A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features |
| title_sort |
bayesian approach to multistage fitting of the variation of the skeletal age features |
| publisher |
Hindawi Limited |
| publishDate |
2018 |
| url |
http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/3802 |
| _version_ |
1717292446688215040 |
| score |
9,463379 |