Skip to main content
SpringerLink
Log in

Segmentation of Epiphysis Region-of-Interest (EROI) using texture analysis and clustering method for hand bone age assessment

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Human hand Bone Age Assessment (BAA) is commonly used by paediatrics for diagnosing the childś health, crime investigation, dead human identification (in case of natural disasters) etc. BAA can be estimated using Epiphysis and Carpal bones of hand X-ray images. These X-ray images may be prone to noise, due to which it might affect the process of background separation and auto-segmentation while performing BAA. Methods existing in the literature of BAA lack in resolving one or more issues (noise reduction, background separation and bone pixel segmentation) individually. This paper proposes a new method to segment Epiphysis bones using the (i) Wavelet packet transformation for noise suppression, (ii) Background suppression using texture features of the image, (iii) Enhancement of bone pixels using histogram equalization and (iv) Finally segmenting the Epiphysis region of interest using clustering method. Performance analysis is performed using two quantitative evaluation methods i.e. supervised and unsupervised evaluation. Unsupervised approach uses Mean Structure Similarity Index (MSSIM) and Homogeneity. Supervised approach uses Precision, Recall, Sensitivity, Accuracy and Error Rate. Proposed method for segmenting the epiphysis bones has shown better accuracy rate of 0.9701.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Alshamrani K, Offiah O (2019) Applicability of the Tanner-Whitehouse 3 method to United Kingdom children born in the 21st century. In 9th International Conference on Children, vol. 7. BioScientifica

  2. Al-Taani AT, Ricketts IW, Cairns AY (1996) Classification of hand bones for bone age assessment. Proceedings of the Third IEEE International Conference on Electronics, Circuits, and Systems. ICECS’96., vol. 2, pp 1088-1091

  3. Bleka Ø, Rolseth V, Dahlberg PS, Saade A, Saade M, Bachs L (2019) BioAlder: a tool for assessing chronological age based on two radiological methods. Int J Legal Med 133(4):1177–1189

    Article  Google Scholar 

  4. Chai HY, Wee LK, Swee TT, Salleh SH (2011) Adaptive crossed reconstructed (acr) k-mean clustering segmentation for computer-aided bone age assessment system. Int J Math Mod Meth Appl Sci 5(3):628–635

    Google Scholar 

  5. Chai HY, Wee LK, Swee TT, Salleh SH, Chia L (2011) An artifacts removal postprocessing for epiphyseal region-of-interest (eroi) localization in automated bone age assessment (baa). Biomed Eng Online 10:87

  6. Cronk C, Schall J, Tanner JM, Marshall WA, Healy MJR, Goldstein H (1987) Assessment of skeletal maturity and prediction of adult height (tw2 method)

  7. Dahlberg PS, Mosdol A, Ding Y, Bleka Rolseth V, Straumann GH, Skjerven-Martinsen M, Delaveris GJ, Vist GE (2019) A systematic review of the agreement between chronological age and skeletal age based on the Greulich and Pyle atlas. Eur Radiol 29(6):2936–2948

    Article  Google Scholar 

  8. Da Silva AMM, Olabarriaga SD, Dietrich CA, Schmitz CA (2001) On determining a signature for skeletal maturity. In Proceedings of XIV Brazilian Symposium on Computer Graphics and Image Processing. IEEE pp 246-251

  9. Dinesh MS, Prakash B, Rao A (1995) Vision system for bone measurement from digital hand radiograph, In Engineering in Medicine and Biology Society, 1995 and 14th Conference of the Biomedical Engineering Society of India. An International Meeting, Proceedings of the First Regional Conference., IEEE, pp SPC9-SP10

  10. El Soufi K, Kabbara Y, Shahin A, Khalil M, Nait-Ali A (2013) Cimor: An automatic segmentation to extract bone tissue in hand x-ray images. In 2013 2nd International Conference on Advances in Biomedical Engineering (ICABME). IEEE pp 171–174

  11. Frisch H, Riedl S, Waldhor T (1996) Computer-aided estimation of skeletal age and comparison with bone age evaluations by the method of greulich-pyle and tanner-whitehouse. Pediatr Radiol 26(3):226–231

    Article  Google Scholar 

  12. Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang HK (2007) Bone age assessment of children using a digital hand atlas. Comput Med Imaging Graph 31(4):322–331

    Article  Google Scholar 

  13. Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang HK (2007) Bone age assessment of children using a digital hand atlas. Comput Med Imaging Graph 31(4–5):322–331

    Article  Google Scholar 

  14. Giordano D, Kavasidis I, Spampinato C (2016) Modeling skeletal bone development with hidden Markov models. Comput Methods Prog Biomed 124:138–147

    Article  Google Scholar 

  15. Greulich WW, Pyle SI (1959) Radiographic atlas of skeletal development of the hand and wrist. Am J Med Sci 238(3):393

    Article  Google Scholar 

  16. Hackman L, Black S (2013) Age estimation from radiographic images of the knee. J Forensic Sci 58(3):732–737

    Article  Google Scholar 

  17. Hackman L, Davies CM, Black S (2013) Age estimation using foot radio graphs from a modern scottish population. J Forensic Sci 58(s1)

  18. Halabi SS, Prevedello LM, Kalpathy-Cramer J, Mamonov AB, Bilbily A, Cicero M, Pan I et al (2019) The RSNA pediatric bone age machine learning challenge. Radiology 29(2):498–503

    Article  Google Scholar 

  19. Han S-H, Kim S-H, Ahn Y-W, Huh G-Y, Kwak D-S, Park D-K, Lee U, Kim Y-S (2009) Microscopic age estimation from the anterior cortex of the femur in Korean adults. J Forensic Sci 54(3):519–522

    Article  Google Scholar 

  20. Hsieh C-W, Liu T-C, Jong T-L, Chen C-Y, Tiu C-M, Chan D-Y (2011) Fast and fully automatic phalanx segmentation using a grayscale-histogram morphology algorithm. Opt Eng 50(8):087007

    Article  Google Scholar 

  21. Hue TTM, Kim JY, Fahriddin M (2011) Hand bone radiograph image segmentation with roi merging, In Proceedings of the 13th IASME/WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing. World Scientific and Engineering Academy and Society (WSEAS) pp 147–154

  22. Iscan MY, Loth SR, Wright RK (1984) Age estimation from the rib by phase analysis: white males. J Forensic Sci 29(4):1094–1104

    Google Scholar 

  23. Jing F, Li M, Zhang H-J, Zhang B (2003) Unsupervised image segmentation using local homogeneity analysis. In Proceedings of the 2003 International Symposium on Circuits and Systems. IEEE vol. 2, p 2

  24. Kashif M, Deserno TM, Haak D, Jonas S (2016) Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment. Comput Biol Med 68:67–75

    Article  Google Scholar 

  25. Kimmerle EH, Konigsberg LW, Jantz RL, Baraybar JP (2008) Analysis of age-at-death estimation through the use of pubic symphyseal data. J Forensic Sci 53(3):558–568

    Article  Google Scholar 

  26. Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, Choy G, Do S (2017) Fully automated deep learning system for bone age assessment. J Digit Imaging 30(4):427–441

    Article  Google Scholar 

  27. Lehmann TM, Abel J, Weiβ C (2006) The impact of lossless image compression to radiographs. International Society for Optics and Photonics In Medical Imaging p 614516

  28. Mahalakshmi BV, Anand MJ (2014) Adaptive wavelet packet decomposition for efficient image denoising by using neighsuren shrink method. Int J Comput Sci Inf Technol 5(4):5003

    Google Scholar 

  29. Martell M, Fescina RH, Martinez E, Bolivar N (1997) Estimation of gestational age by the length of the dorsal spine. J Perinat Med-Official Journal of the WAPM 25(2):168–172

    Article  Google Scholar 

  30. Materka A, Strzelecki M (1998) Texture analysis methods a review. Technical university of lodz, institute of electronics, COST B11 report, Brussels pp 9-11

  31. Michael DJ, Nelson AC (1989) Handx: a model-based system for automatic segmentation of bones from digital hand radiographs. IEEE Trans Med Imaging 8(1):64–69

    Article  Google Scholar 

  32. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7):629–639

    Article  Google Scholar 

  33. Powers DM (2011) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation

  34. Pietka E, McNitt-Gray MF, Kuo ML, Huang HK (1991) Computer assisted phalangeal analysis in skeletal age assessment. IEEE Trans Med Imaging 10(4):616–620

    Article  Google Scholar 

  35. Raj RG (2012) Automated web based system for bone age assessment using historam technique. Malays J Comput Sci 25(3):107–121

    Google Scholar 

  36. Rajith B, Srivastava M, Agarwal S (2016) Edge preserved de-noising method for medical x-ray images using wavelet packet transformation, In Emerging Research in Computing, Information, Communication and Applications. Springer pp 449-467

  37. Rajitha B, Tiwari A, Agarwal S (2015) A new local homogeneity analysis method based on pixel intensities for image defect detection, In IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS) pp 200-206

  38. Rajitha B, Agarwal S (2015) An iterative thresholding method for epiphysis ROI segmentation for radiographic images. In Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering and Technology (ICARCSET 2015). ACM p 16

  39. Ren X, Li T, Yang X, Wang S, Ahmad S, Xiang L, Stone SR et al (2018) Regression convolutional neural network for automated pediatric bone age assessment from hand radiograph. IEEE J Biomed Health Inform 23(5):2030–2038

    Article  Google Scholar 

  40. Schmidt S, Nitz I, Schulz R, Schmeling A (2008) Applicability of the skeletal age determination method of tanner and whitehouse for forensic age diagnostics. Int J Legal Med 122(4):309–314

    Article  Google Scholar 

  41. Schneider MK, Fieguth PW, Karl WC, Willsky AS (2000) Multiscale methods for the segmentation and reconstruction of signals and images. IEEE Trans Image Process 9(3):456–468

    Article  Google Scholar 

  42. Seok J, Kasa-Vubu J, DiPietro M, Girard A (2016) Expert system for automated bone age determination. Expert Syst Appl 50:75–88

    Article  Google Scholar 

  43. Stolojescu-CriSan C, Holban S (2013) A comparison of x-ray image segmentation techniques. Adv Electr Comput Eng 13(3)

  44. Tanner J, Oshman D, Bahhage F, Healy M (1997) Tanner-Whitehouse bone age reference values for North American children. J Pediatr 131(1):34–40

    Article  Google Scholar 

  45. Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2008) The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28(1):52–66

    Article  Google Scholar 

  46. Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2009) The bonexpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28(1):52–66

    Article  Google Scholar 

  47. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  48. Westerberg E (2020) AI-based age estimation using X-ray hand images: A comparison of object detection and deep learning models. BSc thesis, Department of Software Engineering, Blekinge Institute of Technology

  49. Wu E, Kong B, Wang X, Bai J, Lu Y, Gao F, Zhang S, Cao K, Song Q, Lyu S, Yin Y (2019) Residual attention based network for hand bone age assessment. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) pp 1158-1161

  50. Yildiz M, Guvenis A, Guven E, Talat D, Haktan M (2011) Implementation and statistical evaluation of a webbased software for bone age assessment. J Med Syst 35(6):1485–1489

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to express thanks to Dr. K.V.S.S.B.R.K Subramanyam, Retd. Dy. Cheif Medical Officer, The Singereni Collaries Co.Ltd and Dr. Shailendra Kumar Mishra, Medical Officer In-charge, MNNIT Allahabad for their support and kind help in conducting the experiments.

Author information

Authors and Affiliations

  1. Motilal Nehru National Institute of Technology Allahabad, Allahabad, India

    B Rajitha & Suneeta Agarwal

Authors
  1. B Rajitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suneeta Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B Rajitha.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajitha, B., Agarwal, S. Segmentation of Epiphysis Region-of-Interest (EROI) using texture analysis and clustering method for hand bone age assessment. Multimed Tools Appl 81, 1029–1054 (2022). https://doi.org/10.1007/s11042-021-11531-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11531-6

Keywords

Search

Navigation