CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Automated Human Bone Age Assessment using Image Processing Methods - Survey

by Rajitha Bakthula, Suneeta Agarwal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 104 - Number 13
Year of Publication: 2014
Authors: Rajitha Bakthula, Suneeta Agarwal
10.5120/18265-9259

Rajitha Bakthula, Suneeta Agarwal . Automated Human Bone Age Assessment using Image Processing Methods - Survey. International Journal of Computer Applications. 104, 13 ( October 2014), 33-42. DOI=10.5120/18265-9259

@article{ 10.5120/18265-9259,
author = { Rajitha Bakthula, Suneeta Agarwal },
title = { Automated Human Bone Age Assessment using Image Processing Methods - Survey },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 104 },
number = { 13 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 33-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume104/number13/18265-9259/ },
doi = { 10.5120/18265-9259 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:36:05.884953+05:30
%A Rajitha Bakthula
%A Suneeta Agarwal
%T Automated Human Bone Age Assessment using Image Processing Methods - Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 104
%N 13
%P 33-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computers have been widely used in the field of medical research over the past few decades. One of the emerging researches in medical imaging is to estimate age of the live or dead human being. Bone Age Assessment (BAA) is directly proportional to Skeletal (Bone) growth assessment. The study for Age Estimation (AE) has been categorized into two: one from live human where the AE is analysed through radiograph images such as X-ray, MRI, CT and DICOM and another from dead human body remains, where the AE can be performed manually (Forensic Study). AE is purely based on the measuring the length and shape of various bones, so radiographic images are must for live human. These radiographic medical images must be well processed for better assessment using various Image Processing Methods. Therefore, this paper present's an overview of various image processing methods applied for automated bone age estimation of live human and discuss some major challenges in storing the medical image datasets.

References
  1. Liversidge HM, Dean MC, Molleson TI, Increasing human tooth length between birth and 5. 4 years, Am J Phys Anthropol 1993; 90: 307–13.
  2. Hugo F. V. Cardoso, A Test of the Differential Accuracy of the Maxillary Versus the Mandibular Dentition in Age Estimations of Immature Skeletal Remains Based on Developing Tooth Length, J Forensic Sci, March 2007, Vol. 52, No. 2.
  3. Hong-Seop Kho and Young-Ku Kim, Jong-Il Yun, Jeong-Yun Lee, Jin-Woo Chung, Age Estimation of Korean Adults by Occlusal Tooth Wear, J Forensic Sci, May 2007, Vol. 52, No. 3.
  4. Roberto Cameriere, Giuseppe Brogi, Luigi Ferrante, Dora Mirtella, Claudia Vultaggio, Mariano Cingolani and Gino Fornaciari, Reliability in Age Determination by Pulp/Tooth Ratio in Upper Canines in Skeletal Remains, J Forensic Sci, July 2006, Vol. 51, No. 4
  5. Dr. Rajesh Kumar Verma, Dr Mukesh K Goyal, Dr Shiv. Kochar, Age Assessment from Radiological Cranial Suture closure in Fourth to Seventh decades (A Jaipur Based Study), J Indian Acad Forensic Med, 32(2), ISSN 0971-0973.
  6. Vyas P. C. and Sischer, H. Age estimation by closure of suture of skull in individuals of 25-45 yrs of age of Jaipur area. Dissertation for M. D. (Forensic Medicine) University of Rajasthan (1996).
  7. Derya Atamturk and Izzet Duyar, Age-Related Factors in the Relationship Between Foot Measurements and Living Stature and Body Weight, J Forensic Sci, November 2008, Vol. 53, No. 6
  8. Lucina Hackman, Catriona M. Davies, and Sue Black, Age Estimation Using Foot Radiographs from a Modern Scottish Population, J Forensic Sci, January 2013, Vol. 58, No. S1.
  9. Pyle SI, Hoerr NL, A radiographic standard of reference for the growing knee, 1st edn. Springfield, IL: Charles C. Thomas, 1969.
  10. Lucina Hackman and Sue Black, Age Estimation from Radiographic Images of the Knee, J Forensic Sci, May 2013, Vol. 58, No. 3.
  11. Atif Khan, Daciana Iliescu, Evor Hines, Charles Hutchinson, Robert Sneath, Neural Network Based Spinal Age Estimation Using Lumbar Spine Magnetic Resonance Images (MRI), 2013 4th International Conference on Intelligent Systems, Modelling and Simulation.
  12. Martell M, Fescina RH, Martínez E, Bolívar N, Estimation of gestational age by the length of the dorsal spine, J Perinat Med. 1997;25(2):168-72.
  13. A. Schmitt, Age-at-Death assessment using the OsPubis and the Auricular Surface of the llium: a Test on an Identified Asian Sample, Int. J. of Osteoarchaeology:14:1-6(2004).
  14. Erin H. Kimmerle, Lyle W. Konigsberg, Richard L. Jantz and Jose Pablo Baraybar, Analysis of Age-at-Death Estimation Through the Use of Pubic Symphyseal Data, J Forensic Sci, May 2008, Vol. 53, No. 3
  15. Iscan MY, Loth SR, Wright RK, Age estimation from the rib by phase analysis: white males, J Forensic Sci 1984;29:1094–104.
  16. George J. R. Maat, Ann Maes, M. Job Aarent and Nico J. D. Nagelkerke, Histological Age Prediction from the Femur in a Contemporary Dutch Sample The decrease of nonremodeled bone in the anterior cortex, J Forensic Sci, March 2006, Vol. 51, No. 2.
  17. Seung-Ho Han, Sang-Hyun Kim, Yong-Woo Ahn, Gi-Yeong Huh, Dai-Soon Kwak, Dae-Kyoon Park, U-Young Lee and Yi-Suk Kim, Microscopic Age Estimation from the Anterior Cortex of the Femur in Korean Adults, J Forensic Sci, May 2009, Vol. 54, No. 3
  18. Roberto Cameriere, Stefano De Luca, Roberto Biagi, Mariano Cingolani, Giampietro Farronato and Luigi Ferrante, Accuracy of Three Age Estimation Methods in Children by Measurements of Developing Teeth and Carpals and Epiphyses of the Ulna and Radius, J Forensic Sci, 2012, doi: 10. 1111/j. 1556-4029. 2012. 02120, Available online at: onlinelibrary. wiley. co
  19. Sven Schmidt, Inna Nitz, Ronald Schulz and Andreas Schmeling, Applicability of the skeletal age determination method of Tanner and Whitehouse for forensic age diagnostics, Int J Legal Med (2008) 122:309–314
  20. M. Vignolo, S. Milani, E. DiBattista, A. Naselli, M. Mostert, G. Aicardi, Modified Greulich-Pyle, Tanner-Whitehouse and Roche-Wainer-Thissen (knee) methods for skeletal age assessment in a group of Italian children and adolescents, Eur J Pediatr (1990) 149 : 314-317
  21. C. Cattaneo, D. De Anhelis, M. Ruspa, D. Gibelli, R. Cameriere, M. Grandi, How old am I? Age estimation in living adults: a case report. J Forensic Odontostomatol. 2008 Dec 1;26(2):39-43.
  22. Rajendra Kumar Sharma, Demography and Population Problems, Atlantic (2007).
  23. A. Aggrawal and A. Busuttil, Age estimation in the living, The Police Surgeon (Journal of The Association of Police Surgeons). No. 38, Jan 1991, Pp 33-36.
  24. Anil Aggrawal, Estimation of age in the living: in matters civil and criminal, J Anat. 2009 May 11
  25. http://www1. umn. edu/humanrts/research/Philippines/rule_on_children. html
  26. Sundeep Khosla, B. Lawrence Riggs, Pathophysiology of Age-Related Bone Loss and Osteoporosis, Endocrinol Metab Clin N Am, 34 (2005) 1015–1030
  27. http://www. nlm. nih. gov/medlineplus/ency/article/004015. htm
  28. John Wiley & Sons, Inc. , Hoboken, NJ, USA, ASBMR (2008) Chapter 20, Age-Related Bone Loss, in Primer on the Metabolic Bone Diseases and Disorders of Mineral Metabolism, doi: 10. 1002/9780470623992. ch20.
  29. B. Lawrence Riggs, The Burden of Disease: Bone Health, Osteoporosis, and Related Bone Diseases, Report of the Surgeon General's Workshop on Osteoporosis and Bone Health: December 12 – 13, 2002, Washington, DC.
  30. http://medical. nema. org/Dicom/about-DICOM. html
  31. Rafael C. Gonzalez & Richard E. Woods, Digital Image Processing, Addison-Wesley, 2002.
  32. Dinesh M. S, Bhanuprakash, Dr. Ashok Rao, Vision System for bone measurement from digital hand radiographs, Proceedings RC-IEEE-EMBS & 14th BMESl, 1995
  33. W. W. Greulich and S. I. Pyle, Radiographic Atlas of Skeletal Development of Hand Wrist, 2 ed. Palo Alto, CA: Stanford Univ. Press, 19
  34. J. M. Tanner, R. H. Whitehouse, W. A. Marshall, M. J. R. Healy, and H. Goldstein, Assessment of Skeletal Maturity and Prediction of Adult Height (TW2 Method), New York, NY: Academic, 1975.
  35. Tanner JM, Whitehouse RH, Cameron N, Marshall WA, Healy MJR, Goldstein NH. Assessment of skeletal maturity and prediction of adult height (TW3 method), 3rd ed. London: W. B Saunders; 2001.
  36. Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang KH, Bone age assessment of children using digital hand atlas, Computer Medical Imaging Graph 31:322-331, 2007.
  37. University of South California, Image Processing and Informatics Lab: Digital Hand Atlas Database System, Available online at: htto://ipilab. org/BAAweb/
  38. WM. Cameron Chumlea, Alex F. Rouche, David Thissen, The FELS method of assessing the skeletal maturity of the hand-wrist, American. J. of Human Biology, 1:175-183, 1989
  39. David J. Michael, Alan C. Nelson, HANDX: A Model-Based System for Automatic Segmentation of Bones from Digital Hand Radiographs, IEEE Transactions on Medical Imaging, vol 8, No. 1, 1989
  40. Ewa Pietka, Michael F. McNitt-Gray, M. L. Kuo, and H. K. Huang, Computer assisted phalangeal analysis in skeletal Age Assessment, IEEE Transactions on Medical Imaging, vol. 10, No. 4, December 1991.
  41. R. T. Taira, N. J . Mankovich, M. I. Boechat, H. Kangarloo, and H. K. Huang, Design and implementation of a picture archiving and communication system for pediatric radiology, Amer. J . Roentgenol. , vol. 250, pp. 1117-1121, 1988.
  42. S. M. Gam, K. P. Hertzog, A. K. Poznanski, and J. M. Nagy, Metacarpophalangeal length in the evaluation of skeletal malformation, Radiology, vol. 105, pp. 375-381, 1972.
  43. Ewa Pietka, Sylwia Pospiech-Kurkowska, Arkadiusz Gertych, Fei Cao, Integration of computer assisted bone age assessment with clinical PACS, Computerized Medical Imaging and Graphics , 27 (2003) 217–228
  44. A. T. Al-Taani, Classification of Hand Bones for Bone age Assessment, ICECS-1996
  45. H. Frisch, S. Riedl, T. Waldhor, Computer aided estimation of skeletal age and comparition with bone age evaluations by the Greulich-Pyle and tanner-whitehouse, Pediatr Radiol (1996) 26:226-231
  46. F. Caoa, H. K. Huanga, E. Pietkab, V. Gilsanzc, Digital Hand Atlas and web-based bone age assessment system design and implementation, Computerized Medical Imaging and Graphics 24 (2000) 297-307
  47. Sasan Mahmoodi, Bayan S. Sharif, E. Graeme Chester, John P. Owen, and Richard Lee, Skeletal Growth Estimation Using Radiographic Image Processing and Analysis, IEEE Transactions on Information Technology in Bio-Medicine, vol. 4, no. 4, December 2000.
  48. Ewa Pietka, Arkadiusz Gertych, Sylwia Pospiech, Fei Cao, H. K. Huang and V. Gilsanz, Computer-Assisted Bone Age Assessment: Image Preprocessing and Epiphyseal/Metaphyseal ROI Extraction, IEEE Transactions on Medical Imaging, vol 20, No. 8, 2001.
  49. Meindert Niemeijer, B. Van. Ginneken, Casper A. Maas, Frederik J. A. Beek, Max A. Viergever, Assessing the skeletal age from hand radiograph automating the Tanner-Whitehouse method, Proc. SPIE 5032.
  50. Ana Maria Marques Da Silva, Silvia Delgado Olabarriaga, Carlos Augusto Dietrich and Carlos Andre Aita Schmitz,¬ On Determining a Signature for Skeletal Maturity, Proceedings of XIV Brazilian Symposium on Computer Graphics and Image Processing, 2001.
  51. M. K. Schneider, P. W. Fieguth, W. C. Karl and A. S. Willsky, Multiscale methods for the segmentation and reconstruction of signals and images, IEEE Trans. Image Process. vol. 9, no. 3, 2000, pp: 456-468.
  52. Aifeng Zhang, Arkadiusz Gertych and Brent J. Liu, Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones, Comput Med Imaging Graphics, 2007;31(4-5):299-310.
  53. Perona P, Malik J, Scale-space and edge detection using anisotropic diffusion, PAMI 1990:12(7).
  54. Jian Liu, Jing Qi, Zhao Liu, Qin Ning, Xiaoping Luo, Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method, Comput Med Imaging and Graphics 32 (2008) 678–684
  55. Krit Somakanta, Nipon Theera Umpon, Sansanee Auephanwiriyakul, Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression, J. Digit Imaging, 2011, 24:1044-1058.
  56. Gilsanz V, Ratib O, Hand Bone age: A Digital Atlas of Skeletal maturity, 2005.
  57. Hans Henrik Thodberg, Sven Kreiborg, Anders Juul, and Karen Damgaard Pedersen, The BoneXpert Method for Automated Determination of Skeletal Maturity, IEEE Transactions on Medical Imaging, vol 28, No. 1, 2009.
  58. D. Giordano, C. Spampinato, G. Scarciofalo and R. Leonardi, Automated skeletal bone age assessment by integrating EMROI and CROI processing, IEEE International Workshop on Medical Measurements and Applications, 2009, pp:141-145.
  59. Metin Yildiz, Albert Guveni, Esra Guven, Didar Talat and Mahmut Haktan, Implementation and Statistical Evaluation of a Web-Based Software for Bone Age Assessment, J Med Syst, Published Online, 2nd February 2010.
  60. Marjan Mansourvar, Ram Gopal raj, Maizatul Akmar Ismail, Sameem Abdul Kareem, Saravanan Shanmugam, Shahrom Wahid, Rochana Mahmud, RukainiHj, Abdullah, FarizaHanum Nasaruddin and Norsisma Idris, Automated Web based system for bone age assessment using histogram technique, Malaysian Journal of Computer Science, Vol. 25(3), 2012.
  61. Markus Brunk, Heike Ruppertshofen, Sarah Schmidt, peter Beyerlein, Hauke Schramm, Bone age classification using discriminative generalized Hough transform, Springer, Bildverarbeitung für die Medizin 2011, pp. 284-288.
  62. Zhang SY, Liu G, Ma CG, Han YS, Shen XZ, Xu RL, Thodberg HH, Automated Determination of Bone Age in a Modern Chinese Population, ISRN Radiology, Volume 2013, Article ID 874570.
  63. Willems G. A review of the most commonly used dental age estimation techniques. J Forensic Odontostomatol. 2001;19(1):9–17.
  64. Malik P, Saha R, Agarwal A. Applicability of Demirjian's method of age assessment in a North Indian female population, Eur J Paediatr Dent. 2012;13(2):133–135.
  65. https://atlas. dentistry. qmul. ac. uk/
  66. http://www. bridgeheadsoftware. com/products/healthcare_data_management_hdm_platform/
  67. http://www. firehost. com/secure-cloud/enterprise/healthcare
  68. http://ganymed. imib. rwth-aachen. de/irma/datasets_en. php
  69. http://www. magnet. fsu. edu/education/tutorials/magnetacademy/mri/fullarticle. html.
  70. K. Kirk Shung, Diagnostic Ultrasound Imaging and Blood Flow Measurements, CRC Press, 19-Sep-2005.
  71. Concetto Spampinato, Skeletal Bone Age Assessment, University of Catania, Viale Andrea Doria, 6 95125, 1995
Index Terms

Computer Science
Information Sciences

Keywords

Bone Age Assessment Image Processing Automated Bone Growth Assessment Medical Diagnosis Statistical Analysis Computer PACS Fuzzy Logic Neural Network

Powered by PhDFocusTM