Advertisement

Clustering of Various Parameters to Catalog Human Bone Disorders Through Soft Computing Simulation

  • S. Ramkumar
  • R. Malathi
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Every minute nearly 20 fractures occur due to bone disorders in the world. People around the world could not able to differentiate the difference between bone disorders. This chapter is a novel approach toward differentiation of different bone disorders like osteoporosis and osteopenia with influences several parameters. Accordingly, five different parameters such as Calcium, Phosphate, Vitamin D3, Parathyroid hormone (PTH) level, and calcitonin level are considered for the study to categorize the bone disorders. The present approach is an attempt to combine the clinical data measured from each patient and their respective bone mineral density value for the better classification. This is a unique study to provide combined information of both clinical and image processing studies. For this purpose, the above-mentioned parameters and bone density values were observed from ten different patients. All these data were used as an input for soft computing using MATLAB for further processing the data. Initially, unsupervised mapping classifier is adopted to classify bone disorder, for which the clinical parameters are compared with bone density value using k-means clustering algorithm. The prime idea behind using of k-means technique is that the feasibility to classify the inputs based on the distance between the input seeds. With reference to the perpendicular distance between the seed inputs, the bone disorders have been cataloged. The repeated iterations lead to best clustering results.

Keywords

DEXA k-means clustering Unsupervised mapping Classifier Clinical data Bone disorder Centroid distance 

References

  1. 1.
    Shatrugna V, Kulkarni B, Kumar PA, Rani KU, Balakrishna N (2005) Bone status of Indian women from a low-income group and its relationship to the nutritional status. Osteoporos Int 16:1827–1835.  https://doi.org/10.1007/s00198-005-1933-1CrossRefGoogle Scholar
  2. 2.
    Siris ES, Miller PD, Barrett-Connor E, Faulkner KG, Wehren LE, Abbott TA et al (2001) Identification and fracture outcomes of undiagnosed low bone mineral density in postmenopausal women. JAMA 286:2815.  https://doi.org/10.1001/jama.286.22.2815CrossRefGoogle Scholar
  3. 3.
    Park M, Kang B, Jin SJ, Luo S (2009) Computer aided diagnosis system of medical images using incremental learning method. Expert Syst Appl 36:7242–7251.  https://doi.org/10.1016/j.eswa.2008.09.058CrossRefGoogle Scholar
  4. 4.
    Ding F, Leow WK, Sen Howe T (n.d.) Automatic segmentation of femur bones in anterior-posterior pelvis X-ray images. In: Computer vision analysis of images patterns. Springer, Berlin, Heidelberg, pp 205–212.  https://doi.org/10.1007/978-3-540-74272-2_26
  5. 5.
    Lim SE, Xing Y, Chen Y, Leow WK, Sen Howe T, Png MA (2004) Detection of femur and radius fractures in X-ray images. In: Proceedings of the 2nd international conference on advanced medical signal information processing, vol 1. pp 249–256Google Scholar
  6. 6.
    Armato III SG, Sensakovic WF (n.d.) Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis 1.  https://doi.org/10.1016/j.xacra.2004.06.005
  7. 7.
    Pulkkinen P, Jämsä T, Lochmüller E-M, Kuhn V, Nieminen MT, Eckstein F (2008) Experimental hip fracture load can be predicted from plain radiography by combined analysis of trabecular bone structure and bone geometry. Osteoporos Int 19:547–558.  https://doi.org/10.1007/s00198-007-0479-9CrossRefGoogle Scholar
  8. 8.
    Sapthagirivasan V, Anburajan M (2013) Diagnosis of osteoporosis by extraction of trabecular features from hip radiographs using support vector machine: an investigation panorama with DXA. Comput Biol Med 43:1910–1919.  https://doi.org/10.1016/j.compbiomed.2013.09.002CrossRefGoogle Scholar
  9. 9.
    Kavitha MS, Asano A, Taguchi A, Kurita T, Sanada M (2012) Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system. BMC Med Imaging 12:1.  https://doi.org/10.1186/1471-2342-12-1CrossRefGoogle Scholar
  10. 10.
    Detection of osteoporosis and osteopenia using bone densitometer – simulation study. Materials Today: Proceedings (Elsevier) Volume 5, 1024–6Google Scholar
  11. 11.
    Marwaha RK, Tandon N, Garg MK, Kanwar R, Narang A, Sastry A et al (2011) Bone health in healthy Indian population aged 50 years and above. Osteoporos Int 22:2829–2836.  https://doi.org/10.1007/s00198-010-1507-8CrossRefGoogle Scholar
  12. 12.
    Wang L, Nancollas GH, Henneman ZJ, Klein E, Weiner S (2006) Nanosized particles in bone and dissolution insensitivity of bone mineral. Biointerphases 1:106–111.  https://doi.org/10.1116/1.2354575CrossRefGoogle Scholar
  13. 13.
    World Health Organization (2004) WHO scientific group on the assessment of osteoporosis at primary health care level. In: Summary meeting report, pp 5–7Google Scholar
  14. 14.
    Nalavade K, Meshram BB (2014) Evaluation of k-means clustering for effective intrusion detection and prevention in massive network traffic data. Int J Comput Appl 96(7):9–14CrossRefGoogle Scholar
  15. 15.
    Kavitha MS, Asano A, Taguchi A, Kurita T, Sanada M (2012) Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system. BMC Med Imaging 12(1):1CrossRefGoogle Scholar
  16. 16.
    K*-means: an effective and efficient k-means clustering algorithm. IEEE Xplore. Retrieved from  https://doi.org/10.1109/bdcloud-SocialComSustainCom.2016.46. Accessed on 31 Oct 2016
  17. 17.
    McCormick CC (2002) Passive diffusion does not play a major role in the absorption of dietary calcium in normal adults. J Nutr 132:3428–30. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12421863CrossRefGoogle Scholar
  18. 18.
    Kanis JA (2004) WHO scientific group on the assessment of osteoporosis at primary health care level. World Health Organisation, 5–7 May 2004.  https://doi.org/10.1016/s0140-6736(02)08761-5CrossRefGoogle Scholar
  19. 19.
    Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11:586–600.  https://doi.org/10.1109/72.846731CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of EIEAnnamalai UniversityChidambaramIndia
  2. 2.Department of EIEVeltech UniversityChennaiIndia

Personalised recommendations