AUTOMATIC SKELETAL BONE AGE ASSESSMENT: STATE OF THE ART AND FUTURE DIRECTIONS

Isaak Kavasidis, Carmelo Pino

References

  1. [1] G. Milner, R. Levick, and R. Kay, “Assessment of bone age: a comparison of the greulich and pyle, and the tanner and whitehouse methods,” Clinical Radiology, vol. 37, no. 2, pp. 119 – 121, 1986. [Online]. Available: http://www.sciencedirect.com/science/article/pii /S00099260868
  2. [2] R. K. Bull, P. D. Edwards, P. M. Kemp, S. Fry, and I. A. Hughes, “Bone age assessment: a large scale comparison of the Greulich and Pyle, and Tanner and Whitehouse (TW2) methods,” Arch. Dis. Child., vol. 81, pp. 172–173, Aug 1999.
  3. [3] R. Leonardi, D. Giordano, F. Maiorana, and C. Spampinato, “Automatic cephalometric analysis,” Angle Orthod, vol. 78, pp. 145–151, Jan 2008.
  4. [4] F. Cannavo, G. Nunnari, D. Giordano, and C. Spampinato, “Variational method for image denoising by distributed genetic algorithms on grid environment,” in Enabling Technolo294 gies: Infrastructure for Collaborative Enterprises, 2006. WETICE ’06. 15th IEEE International Workshops on, june 2006, pp. 227 –232.
  5. [5] D. J. Michael and A. C. Nelson, “HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs,” IEEE Trans Med Imaging, vol. 8, pp. 64–69, 1989.
  6. [6] E. Pietka, L. Kaabi, M. L. Kuo, and H. K. Huang, “Feature extraction in carpal-bone analysis,” IEEE Trans Med Imaging, vol. 12, pp. 44– 49, 1993.
  7. [7] Z. Liu, J. Chen, J. Liu, and L. Yang, “Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones,” Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on, pp. 445 – 447.
  8. [8] H. Yan Chai, L. Khin Wee, T. Tian Swee, S. H. Salleh, and L. Yee Chia, “An Artifacts Removal Post-processing for Epiphyseal Regionof-interest (EROI) Localization in Automated Bone Age Assessment (BAA),” Biomed Eng Online, vol. 10, p. 87, Sep 2011.
  9. [9] D. Giordano, C. Spampinato, G. Scarciofalo, and R. Leonardi, “An automatic system for skeletal bone age measurement by robust processing of carpal and epiphysial/metaphysial bones,” Instrumentation and Measurement, IEEE Transactions on, vol. 59, no. 10, pp. 2539 –2553, oct. 2010.
  10. [10] A. Zhang, A. Gertych, and B. J. Liu, “Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones,” Computerized Medical Imaging and Graphics, vol. 31, no. 4-5, pp. 299 – 310, 2007, ¡ce:title¿Computer-aided Diagnosis (CAD) and Image-guided Decision Support¡/ce:title¿.
  11. [11] D. Giordano, R. Leonardi, F. Maiorana, G. Scarciofalo, and C. Spampinato, “Epiphysis and metaphysis extraction and classification by adaptive thresholding and DoG filtering for automated skeletal bone age analysis,” Conf Proc IEEE Eng Med Biol Soc, vol. 2007, pp. 6552– 6557, 2007.
  12. [12] R. De Luis-Garcia, M. Martin-Fernandez, J. Arribas, and C. Alberola-Lopez, “A fully automatic algorithm for contour detection of bones in hand radiographs using active contours,” Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on, vol. 3, pp. III – 421– 4 vol.2, sept. 2003.
  13. [13] A. Faro, D. Giordano, and C. Spampinato, “An automated tool for face recognition using visual attention and active shape models analysis,” Conf Proc IEEE Eng Med Biol Soc, vol. 1, pp. 4848–4852, 2006.
  14. [14] H. H. Thodberg, S. Kreiborg, A. Juul, and K. D. Pedersen, “The BoneXpert method for automated determination of skeletal maturity,” IEEE Trans Med Imaging, vol. 28, pp. 52–66, Jan 2009.
  15. [15] L. Bocchi, F. Ferrara, I. Nicoletti, and G. Valli, “An artificial neural network architecture for skeletal age assessment.” pp. 1077–1080, 2003.
  16. [16] H.-H. Lin, S.-G. Shu, Y.-H. Lin, and S.-S. Yu, “Bone age cluster assessment and feature clustering analysis based on phalangeal image rough segmentation,” Pattern Recognition, vol. 45, no. 1, pp. 322 – 332, 2012.
  17. [17] C. W. Hsieh, T. L. Jong, Y. H. Chou, and C. M. Tiu, “Computerized geometric features of carpal bone for bone age estimation,” Chin. Med. J., vol. 120, pp. 767–770, May 2007.
  18. [18] K. Somkantha, N. Theera-Umpon, and S. Auephanwiriyakul, “Bone Age Assessment in Young Children Using Automatic Carpal Bone Feature Extraction and Support Vector Regression,” J Digit Imaging, Feb 2011.
  19. [19] P. Lin, F. Zhang, Y. Yang, and C. Zheng, “Carpal-bone feature extraction analysis in skeletal age assessment based on deformable model,” J. of Computer Science and Technology, 2004.
  20. [20] D. Giordano, C. Spampinato, G. Scarciofalo, and R. Leonardi, “Automatic skeletal bone age assessment by integrating emroi and croi processing,” Medical Measurement and Applications, vol. 0, pp. 141–145, 2009.
  21. [21] A. Tristan-Vega and J. I. Arribas, “A radius and ulna TW3 bone age assessment system,” IEEE 295 Trans Biomed Eng, vol. 55, pp. 1463–1476, May 2008.
  22. [22] C.-W. Hsieh, T.-C. Liu, J.-K. Wang, T.L. Jong, and C.-M. Tiu, “Simplified radius, ulna, and short bone-age assessment procedure using grouped-tanner-whitehouse method,” Pediatrics International, vol. 53, no. 4, pp. 567–575, 2011. [Online]. Available: http://dx.doi.org/10.1111/j.1442200X.2011.03378.x
  23. [23] A. Gooen, E. Hermann, G. Weber, T. Gernoth, T. Pralow, and R.-R. Grigat, “Model-based segmentation of pediatric and adult joints fororthopedic measurements in digital radiographs ofthelowerlimbs,” Computer Science Research and Development, vol. 26, pp. 107–116, 2011, 10.1007/s00450-010-0139-8. [Online]. Available: http://dx.doi.org/10.1007/s00450010-0139-8
  24. [24] A. Faro, D. Giordano, and F. Maiorana, “Mining massive datasets by an unsupervised parallel clustering on a grid: Novel algorithms and case study,” Future Gener. Comput. Syst., vol. 27, pp. 711–724, June 2011. [Online]. Available: http://dx.doi.org/10.1016/j.future.2011.01.002
  25. [25] A. Crisafi, D. Giordano, and C. Spampinato, “Griplab 1.0: Grid image processing laboratory for distributed machine vision applications,” in Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2008. WETICE ’08. IEEE 17th, june 2008, pp. 188 – 191.
  26. [26] C. Spampinato, D. Giordano, R. Di Salvo, Y.-H. J. Chen-Burger, R. B. Fisher, and G. Nadarajan, “Automatic fish classification for underwater species behavior understanding,” in Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams, ser. ARTEMIS ’10. New York, NY, USA: ACM, 2010, pp. 45–50. [Online]. Available: http://doi.acm.org/10.1145/1877868.1877881
  27. [27] H.-J. Mentzel, C. Vilser, M. Eulenstein, T. Schwartz, S. Vogt, J. Bttcher, I. Yaniv, L. Tsoref, E. Kauf, and W. A. Kaiser, “Assessment of skeletal age at the wrist in children with a new ultrasound device,” Pediatric Radiology, vol. 35, pp. 429–433, 2005, 10.1007/s00247-004-1385-3. [Online]. Available: http://dx.doi.org/10.1007/s00247004-1385-3
  28. [28] A. Castriota-Scanderbeg, M. C. Sacco, L. Emberti-Gialloreti, and L. Fraracci, “Skeletal age assessment in children and young adults: comparison between a newly developed sonographic method and conventional methods,” Skeletal Radiology, vol. 27, pp. 271–277, 1998, 10.1007/s002560050380. [Online]. Available: http://dx.doi.org/10.1007/s002560050380
  29. [29] A. Faro, D. Giordano, F. Maiorana, and C. Spampinato, “Discovering genes-diseases associations from specialized literature using the grid,” IEEE Trans Inf Technol Biomed, vol. 13, pp. 554–560, Jul 2009.
  30. [30] A. Faro, D. Giordano, and C. Spampinato, “Combining literature text mining with microarray data: advances for system biology modeling,” Brief Bioinform, Jun 2011. 296

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