A Study on Comparative Analysis of Automated and Semiautomated Segmentation Techniques on Knee Osteoarthritis X-Ray Radiographs

  • Karthiga Nagaraj
  • Vijay JeyakumarEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Arthritis is a most common disease in the worldwide population targeting knee, neck, hand, hip, and almost all the joints of the human body. It is a frequently noticed problem in elder people, especially women. The severity of the disease is analyzed using the older KL grading system. Traditionally, the detection of various grades of OA (osteoarthritis) is interpreted by just a visual examination. A traditional modality, X-ray images are considered as the data for the project. The images are segmented using different segmentation techniques to extract the articular cartilage as region of interest. From the literature, eight different segmentation techniques were identified out of which seven are automated and one is semiautomated. By implementing those techniques and evaluating their performance, it is inferred that block-based segmentation, center rectangle segmentation, and the semiautomated seed point selection segmentation performs well and provides sensitivity, positive prediction value and dice Sorenson’s coefficient of 100%, respectively, and specificity of 0%.


Arthritis X-ray Osteoarthritis Tibio-femoral disk Automated segmentation Semiautomated segmentation 



The images used for this study were obtained from Manisundaram Medical Mission Hospitals, Vellore under the supervision of Dr. Manivannun K., M.S (Ortho.), Mr. Selva Prakash S., Dip. in X-ray Technology, and Ms. Suganya S B.Sc MSW, Counselor. We duly state that my data collection does not involve patient’s interference and invasive protocol.


  1. 1.
    Boniatis I, Costaridou L, Cavouras D, Kalatzis I, Panagiotopoulos E, Panayiotakis G (2006) Osteoarthritis severity of the hip by computer-aided grading of radiographic images. Med Biol Eng Compu 44(9):793CrossRefGoogle Scholar
  2. 2.
    Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ (2006) Fuzzy C-Means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15CrossRefGoogle Scholar
  3. 3.
    Deokar DD, Patil CG (2015) Effective feature extraction based automatic knee osteoarthritis detection and classification using neural network. Int J Eng Techn 1(3)Google Scholar
  4. 4.
    Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-Means clustering algorithm and subtractive clustering algorithm. Procedia Comput Sci 54(2015):764–771CrossRefGoogle Scholar
  5. 5.
    Gan HS, Sayuti KA (2017) Comparison of improved semi-automated segmentation technique with manual segmentation: data from the osteoarthritis initiativeGoogle Scholar
  6. 6.
    Gornale SS, Patravali PU Manza RR (2016) Detection of osteoarthritis using knee X-ray image analyses: a machine vision based approach. Int J Comput Appl 145(1)Google Scholar
  7. 7.
    Hill PR, Canagarajah CN, Bull DR (2003) Image segmentation using a texture gradient based watershed transform. IEEE Trans Image Process 12(12):1618–1633MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kubkaddi S, Ravikumar KM (2017) Early detection of knee osteoarthritis using SVM classifier. IJSEAT 5(3):259–262Google Scholar
  9. 9.
    Lankton S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Trans Image Process 17(11):2029–2039MathSciNetCrossRefGoogle Scholar
  10. 10.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  11. 11.
    Lu S, Wang S, Zhang Y (2017) A note on the marker-based watershed method for X-ray image segmentation. Comput Methods Programs Biomed 141:1–2CrossRefGoogle Scholar
  12. 12.
    Minciullo L, Cootes T (2016) Fully automated shape analysis for detection of Osteoarthritis from lateral knee radiographs. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE, pp 3787–3791Google Scholar
  13. 13.
    Navale DI, Hegadi RS, Mendgudli N (2015) Block based texture analysis approach for knee osteoarthritis identification using SVM. In: 2015 IEEE international WIE conference on electrical and computer engineering (WIECON-ECE). IEEE, pp 338–341Google Scholar
  14. 14.
    Øiestad BE, Juhl CB, Eitzen I, Thorlund JB (2015) Knee extensor muscle weakness is a risk factor for development of knee osteoarthritis. A systematic review and meta-analysis. Osteoarthritis Cartilage 23(2):171–177CrossRefGoogle Scholar
  15. 15.
    Rahman MA, Liu S, Lin S, Wong C, Jiang G, Kwok N (2015) Image contrast enhancement for brightness preservation based on dynamic stretching. Int J Image Process (IJIP) 9(4):241Google Scholar
  16. 16.
    Roopa H, Asha T (2016). Segmentation of X-ray image using city block distance measure. In: 2016 International conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, pp 186–189Google Scholar
  17. 17.
    Ryzhkov MD (2015) Knee cartilage segmentation algorithms: a critical literature review. Master’s thesisGoogle Scholar
  18. 18.
    Scott D, Kowalczyk A (2007) Osteoarthritis of the knee. BMJ Clin Evid 2007:1121Google Scholar
  19. 19.
    Shamir L, Ling SM, Scott Jr, WW, Bos A, Orlov N, Macura TJ,… Goldberg IG (2009) Knee X-ray image analysis method for automated detection of Osteoarthritis. IEEE Trans Biomed Eng 56(2):407–415CrossRefGoogle Scholar
  20. 20.
    Wahyuningrum RT, Anifah L, Purnama IKE, Purnomo MH (2016) A novel hybrid of S2DPCA and SVM for knee osteoarthritis classification. In: 2016 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA). IEEE, pp 1–5Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringSSN College of EngineeringChennaiIndia

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