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Bone age estimation based on phalanx information with fuzzy constrain of carpals

Medical & Biological Engineering & Computing volume 45, pages 283–295 (2007)Cite this article

Abstract

The proposed automatic bone age estimation system was based on the phalanx geometric characteristics and carpals fuzzy information. The system could do automatic calibration by analyzing the geometric properties of hand images. Physiological and morphological features are extracted from medius image in segmentation stage. Back-propagation, radial basis function, and support vector machine neural networks were applied to classify the phalanx bone age. In addition, the proposed fuzzy bone age (BA) assessment was based on normalized bone area ratio of carpals. The result reveals that the carpal features can effectively reduce classification errors when age is less than 9 years old. Meanwhile, carpal features will become less influential to assess BA when children grow up to 10 years old. On the other hand, phalanx features become the significant parameters to depict the bone maturity from 10 years old to adult stage. Owing to these properties, the proposed novel BA assessment system combined the phalanxes and carpals assessment. Furthermore, the system adopted not only neural network classifiers but fuzzy bone age confinement and got a result nearly to be practical clinically.

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References

  1. Cao F, Huang HK, Pietka E, Gilsanz V (2000) Digital hand atlas and web-based bone age assessment: system design and implementation. Comput Med Imag Graph 24:297–307

    Article  Google Scholar 

  2. Cao F, Huang HK, Pietka E, Gilsanz V, Dey PS, Gertych A (2002) An image database for digital hand atlas and its application to other PACS-based CAD servers. Radiology 225:139

    Google Scholar 

  3. Cope RK, Rockett PI (2000) Efficacy of Gaussian smoothing in Canny edge detector. Electron Lett 36:1615–1617

    Article  Google Scholar 

  4. Greulich WW, Pyle SI (1959) Radiographic atlas of skeletal development of the hand and wrist. Stanford University Press, Stanford

    Google Scholar 

  5. Haykin S (1999) Neural networks, 2nd edn. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  6. Mahmoodi S, Sharif BS, Chester EG, Owen JP, Lee REJ (1997) Automated vision system for skeletal age assessment using knowledge based techniques. IPA97 2:809–813

    Google Scholar 

  7. Mahmoodi S, Sharif BS, Chester EG, Owen JP, Lee R (2000) Skeletal growth estimation using radiographic image processing and analysis. IEEE Trans Inf Technol Biomed 4:292–297

    Article  Google Scholar 

  8. Marques Da Silva AM, Delgado Olabarriaga S, Dietrich CA, Schmitz CAA (2001) On determining a signature for skeletal maturity. In: CGIP2001 Proceedings, pp 246–251

  9. Molinari L, Gasser T, Largo RH (2004) Bone age: RUS/CB and gender differences of percentiles for score and score increments. Ann Hum Biol 31:421–435

    Article  Google Scholar 

  10. Mora S, Boechat MI, Pietka E, Huang HK, Gilsanz V (2001) Skeletal age determinations in children of European and African descent: applicability of the Greulich and Pyle standards. Pediatr Res 50:624–628

    Google Scholar 

  11. Pietka E, McNitt-Gray MF, Kuo ML, Huang HK (1991) Computer-assisted phalangeal analysis in skeletal age assessment. IEEE Trans Med Imaging 10:44–49

    Article  Google Scholar 

  12. Pietka E, Kaabi L, Kuo ML, Huang HK (1993) Feature extraction in carpal-bone analysis. IEEE Trans Med Imaging 12:616–620

    Article  Google Scholar 

  13. Pietka E, Gertych A, Pospiech S, Cao F, Huang HK, Gilsanz V (2001) Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction. IEEE Trans Med Imaging 20:715–729

    Article  Google Scholar 

  14. Pietka E, Pospiech S, Gertych A, Cao F, Huang HK, Gilsanz V (2001) Computer automated approach to the extraction of epiphyseal regions in hand radiographs. J Digit Imaging 14:165–172

    Article  Google Scholar 

  15. Pietka E, Pospiech-Kurkowska S, Gertych A, Cao F (2003) Integration of computer assisted bone age assessment with clinical PACS. Comput Med Imaging Graph 27:217–228

    Article  Google Scholar 

  16. Pietka E, Gertych A, Pospiech-Kurkowska S, Cao F, Huang HK, Gilzanz V (2004) Computer-assisted bone age assessment: graphical user interface for image processing and comparison. J Digit Imaging 17:175–188

    Article  Google Scholar 

  17. Press WH, Flannery BP, Teukoisky SA, Vertterling WT (1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  18. Proos LA, Lonnerholm T, Jonsson B, Tuvemo T (2006) Can bone age determination provide criteria for growth hormone treatment in adopted girls with early puberty? A comparison of the Greulich-Pyle and the Tanner-Whitehouse 2 methods. Ups J Med Sci 111:117–129

    Article  Google Scholar 

  19. Sankar KP, Sushmita M (1999) Neural-fuzzy pattern recognition: methods in soft computing. Wiley, New York

    Google Scholar 

  20. Sato K, Mito T, Mitani H (2001) An accurate method of predicting mandibular growth potential based on bone maturity. Am J Orthod Dentofac Orthop 120:286–293

    Article  Google Scholar 

  21. Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision, 2nd edn. PWS Press, Pacific Grove

    Google Scholar 

  22. Vidulich L, Norris SA, Cameron N, Pettifor JM (2006) Differences in bone size and bone mass between black and white 10-year-old South African children. Osteoporosis Int 17:433–440

    Article  Google Scholar 

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Affiliations

  1. Department of Electrical Engineering, National Tsing Hwa University, Hsinchu, Taiwan, ROC

    Chi-Wen Hsieh & Tai-Lang Jong

  2. Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC

    Chui-Mei Tiu

Authors
  1. Chi-Wen Hsieh
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  2. Tai-Lang Jong
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  3. Chui-Mei Tiu
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Correspondence to Chi-Wen Hsieh.

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Hsieh, CW., Jong, TL. & Tiu, CM. Bone age estimation based on phalanx information with fuzzy constrain of carpals. Med Bio Eng Comput 45, 283–295 (2007). https://doi.org/10.1007/s11517-006-0155-9

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  • DOI: https://doi.org/10.1007/s11517-006-0155-9

Keywords

  • Bone age
  • Medius
  • Carpal
  • Phalanx