Journal of Digital Imaging

, Volume 32, Issue 2, pp 300–313 | Cite as

Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image

  • Oishila BandyopadhyayEmail author
  • Arindam Biswas
  • Bhargab B. Bhattacharya


Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-cost diagnostic tool for diagnosis and visualization of bone cancer. In this paper, a novel technique for the assessment of cancer stage and grade in long bones based on X-ray image analysis has been proposed. Cancer-affected bone images usually appear with a variation in bone texture in the affected region. A fusion of different methodologies is used for the purpose of our analysis. In the proposed approach, we extract certain features from bone X-ray images and use support vector machine (SVM) to discriminate healthy and cancerous bones. A technique based on digital geometry is deployed for localizing cancer-affected regions. Characterization of the present stage and grade of the disease and identification of the underlying bone-destruction pattern are performed using a decision tree classifier. Furthermore, the method leads to the development of a computer-aided diagnostic tool that can readily be used by paramedics and doctors. Experimental results on a number of test cases reveal satisfactory diagnostic inferences when compared with ground truth known from clinical findings.


Bone cancer Bone X-ray Connected component Decision tree Ortho-convex cover Runs-test Support vector machine 



The first author would like to acknowledge the Department of Science & Technology, Government of India, for providing financial support to her vide grant no. SR/WOS-A/ET-1022/2014. Some X-ray test images used in this work are taken from the website of TCIA (; Radiology Assistant (; Radiopedia (; Bone and Spine (; etc. We acknowledge all of them with thanks.


  1. 1.
    American Joint Committee on Cancer (AJCC) AJCC cancer staging manual vol 17(6). Springer-Verlag (2010)Google Scholar
  2. 2.
    Alelyani S, Tang J, Liu H: Feature selection for clustering: a review. In: Data clustering: Algorithms and applications, vol 29, pp 110–121. CRC Press, 2013Google Scholar
  3. 3.
    Bandyopadhyay O, Biswas A, Bhattacharya BB: Long-bone fracture detection in digital X-ray images based on digital-geometric techniques. Comput Methods Programs Biomed 123:2–14, 2016Google Scholar
  4. 4.
    Bandyopadhyay O, Chanda B, Bhattacharya BB: Automatic segmentation of bones in X-ray images based on entropy measure. Int J Image Graph 16(1):1650,001–32, 2016Google Scholar
  5. 5.
    Bennett JR, Donald JSM: On the measurement of curvature in a quantized environment. IEEE Trans Comput 24(8):803–820, 1975Google Scholar
  6. 6.
    Bourouis S, Chennoufi I, Hamrouni K: Multimodal bone cancer detection using fuzzy classification and variational model. In: Proceedings, IAPR, vol LNCS 8258, pp 174–181, 2013Google Scholar
  7. 7.
    Brant WE, Helms CA: Fundamentals of diagnostic radiology Philadelphia: Wolters Kluwer, 2007Google Scholar
  8. 8.
    Burges CJC: A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167, 1998Google Scholar
  9. 9.
    Criminisiand A, Shotton J: Decision forests for computer vision and medical image analysis London: Springer, 2013CrossRefGoogle Scholar
  10. 10.
    Dillencourt MB, Samet H: A general approach to connected component labeling for arbitrary image representations. J ACM 39:253–280, 1992Google Scholar
  11. 11.
    Ehara S: MR Imaging in staging of bone tumors. J Cancer Imaging 6:158–162, 2006Google Scholar
  12. 12.
    Frangi A, Egmont-Petersen M, Niessen W, Reiber J, Viergever M: Segmentation of bone tumor in MR perfusion images using neural networks and multiscale pharmacokinetic features. Image Vis Comput 19:679–690, 2001Google Scholar
  13. 13.
    Freeman H: On the encoding of arbitrary geometric configurations. IRE Trans Electron Comput 10:260–268, 1961Google Scholar
  14. 14.
    Hastie T, Tibshirani R, Friedman J: The elements of statistical learning London: Springer, 2009CrossRefGoogle Scholar
  15. 15.
    Heidl W, Thumfart S, Lughofer E, Eitzinger C, Klement EP: Machine learning based analysis of gender differences in visual inspection decision making. Inf Sci 224:62–76, 2013Google Scholar
  16. 16.
    Heymann D: Bone cancer Cambridge: Academic Press, 2015Google Scholar
  17. 17.
    Hsu C, Chang C, Lin C (2003) Practical guide to support vector classification.
  18. 18.
    Huang S, Chiang K: Automatic detection of bone metastasis in vertebrae by using CT images. In: Proceedings, world congress on engineering, vol 2, pp 1166–1171, 2012Google Scholar
  19. 19.
    Leven RI, Rubin DS, Rastogi S, Siddiqui MS: Statistics for management India: Pearson, 2012Google Scholar
  20. 20.
    Maulik U, Chakraborty D: Fuzzy preference based feature selection and semisupervised SVM for cancer classification. IEEE Trans NanoBiosciences 13(2):152–160, 2014Google Scholar
  21. 21.
    Nandy SC, Mukhopadhyay K, Bhattacharya BB: Recognition of largest empty orthoconvex polygon in a point set. Inf Process Lett 110(17):746–752, 2010Google Scholar
  22. 22.
    Nisthula P, Yadhu RB: A novel method to detect bone cancer using image fusion and edge detection. Int J Eng Comput Sci 2(6):2012–2018, 2013Google Scholar
  23. 23.
    Ping YY, Yin CW, Kok LP: Computer aided bone tumor detection and classification using X-ray images. In: Proceedings, international federation for medical and biological engineering, pp 544–557, 2008Google Scholar
  24. 24.
    Popovic A, Fuente MDL, Engelhardt M, Radermacher K: Statistical validation metric for accuracy assessment in medical image segmentation. International Journal of Computer Assisted Radiology and Surgery 2(4):169–181, 2007Google Scholar
  25. 25.
    Powers D: Evaluation: From precision, recall and F-Measure to ROC, Informedness, Markedness & Correlation. J Mach Learn Technol 2(1):37–63, 2007Google Scholar
  26. 26.
    Roobaert D, Karakoulas G, Chawla NV (2006) Information gain correlation and support vector machinesGoogle Scholar
  27. 27.
    Vapnik VN, Chervonenkis AY: On the uniform convergence of relative frequencies of events to their probabiloties. Theory of Probability and its Applications 16(2):264–280, 1971Google Scholar
  28. 28.
    Yao J, O’Connor SD, Summer R: Computer aided lytic bone metastasis detection using regular CT images. In: Proceedings, SPIE medical imaging, pp 1692–1700, 2006Google Scholar
  29. 29.
    Zhu W, Zeng N, Wang W: Sensitivity, specificity, accuracy, associated confidence interval and roc analysis with practical SAS® implementations. In: Proceedings NESUG: Health and Life Sciences, pp 1–9, 2010Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Information Technology KalyaniKalyaniIndia
  2. 2.Department of Information TechnologyIndian Institute of Engineering Science and TechnologyShibpurIndia
  3. 3.Advanced Computing and Microelectronics UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations