Construction of a knee osteoarthritis diagnostic system based on X-ray image processing

  • Yongping Li
  • Ning Xu
  • Qiang Lyu


In order to accurately diagnose knee osteoarthritis, a detection technique as well as its quantitative assessment based on X-ray image processing is proposed in this study. First, image segmentation is implemented on the basis of maximum between-class variance and region growing method. Second, the edge of the image concerned is filled based on calculations of mathematical morphology, followed by edge extraction, which realizes extraction of the image in the region of interest. Finally, processing and judgment concerning four indicators to determine knee osteoarthritis, namely, joint space asymmetry, articular sclerosis, rugged articular surface, and intra-articular loose bodies, were judged and judged. Our experimental results show that this technique can effectively detect and describe the features of knee osteoarthritis, which can be used as a tool for clinical diagnosis.


Knee joint Osteoarthritis X-ray Image processing Machine vision 



The project is funded by Zhejiang Science and Technology Department Public Welfare Project (Grant: 2017C35001) and Ningbo Municipal Bureau of Science and Technology Project (Grants: 2017A10027, 2017C50023, 2016C10056).


  1. 1.
    Jiang, Y., Hua, Q., Ren, J., Zeng, F., Sheng, J., Liao, H., et al.: Eosinophilic hyperplastic lymphogranuloma: clinical diagnosis and treatment experience of 41 cases. Am. J. Otolaryngol. 38(5), 626 (2017)CrossRefGoogle Scholar
  2. 2.
    Dona, A.C., Coffey, S., Figtree, G.: Translational and emerging clinical applications of metabolomics in cardiovascular disease diagnosis and treatment. Eur. J. Prev. Cardiol. 23(15), 1578–1589 (2016)CrossRefGoogle Scholar
  3. 3.
    Allsop, M.J., Twiddy, M., Grant, H., Czoski-Murray, C., Mon-Williams, M., Mushtaq, F., et al.: Diagnosis, medication, and surgical management for patients with trigeminal neuralgia: a qualitative study. Acta Neurochir. 157(11), 1925–1933 (2015)CrossRefGoogle Scholar
  4. 4.
    Williams, B.T., Ahrberg, A.B., Goldsmith, M.T., Campbell, K.J., Shirley, L., Wijdicks, C.A., et al.: Ankle syndesmosis: a qualitative and quantitative anatomic analysis. Am. J. Sports Med. 43(1), 88–97 (2015)CrossRefGoogle Scholar
  5. 5.
    De, G.A., Watson, S., Ellis, L.M., Rodón, J., Tabernero, J., De, G.A., et al.: Pragmatic issues in biomarker evaluation for targeted therapies in cancer. Nat. Rev. Clin. Oncol. 12(4), 197–212 (2015)CrossRefGoogle Scholar
  6. 6.
    Krych, A.J., Sousa, P.L., King, A.H., Engasser, W.M., Stuart, M.J., Levy, B.A.: Meniscal tears and articular cartilage damage in the dislocated knee. Knee Surg. Sports Traumatol. Arthrosc. 23(10), 3019–3025 (2015)CrossRefGoogle Scholar
  7. 7.
    Hassan, E.B., Mirams, M., Ghasemzadeh, A., Mackie, E.J., Whitton, R.C.: Role of subchondral bone remodelling in collapse of the articular surface of thoroughbred racehorses with palmar osteochondral disease. Equine Vet. J. 48(2), 228 (2016)CrossRefGoogle Scholar
  8. 8.
    Thijssen, E., Van, C.A., Pm, V.D.K.: Obesity and osteoarthritis, more than just wear and tear: pivotal roles for inflamed adipose tissue and dyslipidaemia in obesity-induced osteoarthritis. Rheumatology. 54(4), 588 (2015)CrossRefGoogle Scholar
  9. 9.
    Horváth, Ádám, Tékus, V., Boros, M., Pozsgai, G., Botz, B., Borbély, Éva, et al.: Transient receptor potential ankyrin 1 (TRPA1) receptor is involved in chronic arthritis: in vivo study using TRPA1-deficient mice. Arthritis Res. Ther. 18(1), 6 (2016)CrossRefGoogle Scholar
  10. 10.
    Shah, F.A., Palmquist, A.: Evidence that osteocytes in autogenous bone fragments can repair disrupted canalicular networks and connect with osteocytes in de novo, formed bone on the fragment surface. Calcif. Tissue Int. 101(3), 321–327 (2017)CrossRefGoogle Scholar
  11. 11.
    Endrizzi, M., Basta, D., Olivo, A.: Laboratory-based X-ray phase-contrast imaging with misaligned optical elements. Appl. Phys. Lett. 107(12), 23–26 (2015)CrossRefGoogle Scholar
  12. 12.
    Sarapata, A., Fingerle, A., Braun, C., Pfeiffer, F., Herzen, J., Kaiser, K., et al.: Quantitative imaging using high-energy X-ray phase-contrast CT with a 70 kVp polychromatic X-ray spectrum. Opt. Express. 23(1), 523 (2015)CrossRefGoogle Scholar
  13. 13.
    Zhang, J., Zhou, G., Tian, D., Lin, R., Peng, G., Su, M.: Microdissection of human esophagogastric junction wall with phase-contrast X-ray CT imaging. Sci. Rep. 5(5), 13831 (2015)CrossRefGoogle Scholar
  14. 14.
    Chuklin, P., Chalermpanapan, V., Nookeaw, T., Saithong, S., Chainok, K., Phongpaichit, S., et al.: Synthesis, X-ray structure of organometallic ruthenium (II) p-cymene complexes based on P- and N-donor ligands and their in vitro antibacterial and anticancer studies. J. Organomet. Chem. 846, 242–250 (2017)CrossRefGoogle Scholar
  15. 15.
    Li, K., Etschmann, B., Rae, N., Reith, F., Ryan, C.G., Kirkham, R., et al.: Ore petrography using megapixel X-ray imaging: rapid insights into element distribution and mobilization in complex Pt and U-Ge-Cu ores. Econ. Geol. 111(2), 487–501 (2016)CrossRefGoogle Scholar
  16. 16.
    Shen, J., Chen, P., Su, L., Shi, T., Tang, Z., Liao, G.: X-ray inspection of TSV defects with self-organizing map network and Otsu algorithm. Microelectron. Reliab. 67, 129–134 (2016)CrossRefGoogle Scholar
  17. 17.
    Begelman, M.C., Armitage, P.J., Reynolds, C.S.: Accretion disk dynamo as the trigger for X-ray binary state transitions. Astrophys. J. 809(2), 118 (2015)CrossRefGoogle Scholar
  18. 18.
    Churazov, E., Vikhlinin, A., Sunyaev, R.: (No) dimming of X-ray clusters beyond z ~ 1 at fixed mass: crude redshifts and masses from raw X-ray and SZ data. Mon. Not. R. Astron. Soc. 450(2), 1984–1989 (2015)CrossRefGoogle Scholar
  19. 19.
    Zhuge, X., Palenstijn, W.J., Batenburg, K.J.: TVR-DART: a more robust algorithm for discrete tomography from limited projection data with automated gray value estimation. IEEE Trans. Image Process. 25(1), 455–468 (2015)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Chies, L.A.S., Rodr, B.P.D.G., Arag, A.S.N., Bamford, P.S., Gray, E.M., Wolf, C., et al.: OMEGA–OSIRIS mapping of emission-line galaxies in A901/2–I. Survey description, data analysis, and star formation and AGN activity in the highest density regions. Mon. Not. R. Astron. Soc. 450(4), 4458 (2015)CrossRefGoogle Scholar
  21. 21.
    Banerjee, S., Mitra, S., Shankar, B.U.: Single seed delineation of brain tumor using multi-thresholding. Inf. Sci. 330(C), 88–103 (2016)CrossRefGoogle Scholar
  22. 22.
    Lehermeier, C., Teyssèdre, S., Schön, C.C.: Genetic gain increases by applying the usefulness criterion with improved variance prediction in selection of crosses. Genetics. 207(4), 1651 (2017)Google Scholar
  23. 23.
    Malek, A.A., Wan, E.Z.W.A.R., Ibrahim, A., Mahmud, R., Yasiran, S.S., Jumaat, A.K.: Region and boundary segmentation of microcalcifications using seed-based region growing and mathematical morphology. Proc. Soc. Behav. Sci. 8(1), 634–639 (2010)CrossRefGoogle Scholar
  24. 24.
    Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the sobel operator. IEEE J. Solid-State Circuits. 23(2), 358–367 (2002)CrossRefGoogle Scholar
  25. 25.
    Liao, T., Li, X., Xu, G., Zhang, Y.J.: Secondary laplace operator and generalized Giaquinta–Hildebrandt operator with applications on surface segmentation and smoothing. Comput. Aided Des. 70(C), 56–66 (2016)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Zheng, Y., Zhou, Y., Zhou, H., Gong, X.: Ultrasound image edge detection based on a novel multiplicative gradient and canny operator. Ultrason. Imaging. 37(3), 238–250 (2015)CrossRefGoogle Scholar
  27. 27.
    Yoshimoto, H.: Image processing apparatus, display apparatus, and image processing method. J. Oral Rehabil. 98(1), 231–233 (2014)MathSciNetGoogle Scholar
  28. 28.
    Qin, H.B., Zhu, J.M., Lin, Z.Q., Xu, W.P., Tan, D.C., Zheng, L.R., et al.: Selenium speciation in seleniferous agricultural soils under different cropping systems using sequential extraction and X-ray absorption spectroscopy. Environ. Pollut. 225, 361–369 (2017)CrossRefGoogle Scholar
  29. 29.
    Buchmueller, O., Dolan, M.J., Malik, S.A., Mccabe, C.: Characterising dark matter searches at colliders and direct detection experiments: vector mediators. J. High Energy Phys. 2015(1), 37 (2015)CrossRefGoogle Scholar
  30. 30.
    Chen, Y., Guan, G., Matsushita, S., Li, X.: Robust stochastic gradient-based adaptive filtering algorithms to realize compressive sensing against impulsive interferences. In: Control and Decision Conference. IEEE (2016)Google Scholar
  31. 31.
    Schawaller, M., Schenck, K., Hoffmeister, S.A., Schaller, H., Schaller, H.C.: On the convergence of alternating direction Lagrangian methods for nonconvex structured optimization problems. IEEE Trans. Control Netw. Syst. 3(3), 296–309 (2016)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Neacsiu, A.D., Wardciesielski, E.F., Linehan, M.M.: Emerging approaches to counseling intervention: dialectical behavior therapy. Couns. Psychol. 40(7), 1003–1032 (2016)CrossRefGoogle Scholar
  33. 33.
    Zhang, T., Yang, X., Hu, S., Su, F.: Extraction of coastline in aquaculture coast from multispectral remote sensing images: object-based region growing integrating edge detection. Remote Sens. 5(9), 4470–4487 (2013)CrossRefGoogle Scholar
  34. 34.
    Pantic, I., Nesic, Z., Pantic, J.P., Radojevićškodrić, S., Cetkovic, M., Jovanovic, G.B.: Fractal analysis and gray level co-occurrence matrix method for evaluation of reperfusion injury in kidney medulla. J. Theor. Biol. 397(2), 61–67 (2016)CrossRefGoogle Scholar
  35. 35.
    Fujita, A., Buch, K., Li, B., Kawashima, Y., Qureshi, M.M., Sakai, O.: Difference between HPV-positive and HPV-negative non-oropharyngeal head and neck cancer: texture analysis features on CT. J. Comput. Assist. Tomogr. 40(1), 43 (2016)CrossRefGoogle Scholar
  36. 36.
    Roach, B.L., Hung, C.T., Cook, J.L., Ateshian, G.A., Tan, A.R.: Fabrication of tissue engineered osteochondral grafts for restoring the articular surface of diarthrodial joints. Methods. 84, 103–108 (2015)CrossRefGoogle Scholar
  37. 37.
    Dibbern, K., Kempton, L.B., Higgins, T.F., Morshed, S., Mckinley, T.O., Marsh, J.L., et al.: Fractures of the tibial plateau involve similar energies as the tibial pilon but greater articular surface involvement. J. Orthop. Res. 35(3), 618–624 (2017)CrossRefGoogle Scholar
  38. 38.
    Cigan, A.D., Durney, K.M., Nims, R.J., Vunjaknovakovic, G., Hung, C.T., Ateshian, G.A.: Nutrient channels aid the growth of articular surface-sized engineered cartilage constructs. Tissue Eng. Part A. 22(17), 1063–1074 (2016)CrossRefGoogle Scholar
  39. 39.
    Wu, T., Wu, H., Du, Y., Kwok, N., Peng, Z.: Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features. Wear. 316(1–2), 19–29 (2014)CrossRefGoogle Scholar
  40. 40.
    Skoura, A., Nuzhnaya, T., Megalooikonomou, V.: Integrating edge detection and fuzzy connectedness for automated segmentation of anatomical branching structures. Int. J. Bioinf. Res. Appl. 10(1), 93–109 (2014)CrossRefGoogle Scholar
  41. 41.
    Varga, B., Karacs, K.: Towards a balanced trade-off between speed and accuracy in unsupervised data-driven image segmentation. Mach. Vis. Appl. 24(6), 1267–1294 (2013)CrossRefGoogle Scholar
  42. 42.
    Javadi, M., Azar, S. M., Azami, S., Ghidary, S.S., Sadeghnejad, S., Baltes, J.: Humanoid robot detection using deep learning: a speed-accuracy tradeoff. In: The Robocup International Symposium (2017)Google Scholar
  43. 43.
    Michetti, J., Georgelin-Gurgel, M., Mallet, J.P., Diemer, F., Boulanouar, K.: Influence of cone beam CT parameters on the output of an automatic edge-detection based endodontic segmentation. Dentomaxillofac. Radiol. 44(8), 20140413 (2015)CrossRefGoogle Scholar
  44. 44.
    Cheung, Y.M., Li, M., Cao, X., You, X.: Lip segmentation under map-MRF framework with automatic selection of local observation scale and number of segments. IEEE Trans. Image Process. 23(8), 3397–3411 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Chen, R., Xu, J., Chen, H., Su, J., Zhang, Z., Chen, K.: Accurate calibration method for camera and projector in fringe patterns measurement system. Appl. Opt. 55(16), 4293 (2016)CrossRefGoogle Scholar
  46. 46.
    Archibald, R., Hu, J., Gelb, A., Farin, G.: Improving the accuracy of volumetric segmentation using pre-processing boundary detection and image reconstruction. IEEE Trans. Image Process. 13(4), 459–466 (2004)CrossRefGoogle Scholar
  47. 47.
    Schorsch, S., Hours, J.H., Vetter, T., Mazzotti, M., Jones, C.N.: An optimization-based approach to extract faceted crystal shapes from stereoscopic images. Comput. Chem. Eng. 75, 171–183 (2015)CrossRefGoogle Scholar
  48. 48.
    Gain, A.L., Carroll, J., Paulino, G.H., Lambros, J.: A hybrid experimental/numerical technique to extract cohesive fracture properties for mode-I fracture of quasi-brittle materials. Int. J. Fract. 169(2), 113–131 (2015)CrossRefzbMATHGoogle Scholar
  49. 49.
    Wang, Q., Niemi, J., Tan, C.M., You, L., West, M.: Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy. Cytom. Part A. 77A(1), 101–110 (2010)Google Scholar
  50. 50.
    Merazi-Meksen, T., Boudraa, M., Boudraa, B.: Mathematical morphology for TOFD image analysis and automatic crack detection. Ultrasonics. 54(6), 1642–1648 (2014)CrossRefzbMATHGoogle Scholar
  51. 51.
    Matsukuma, S., Takeo, H., Okada, K., Sato, K.: Fatty lesions in intra-articular loose bodies: a histopathological study of non-primary synovial chondromatosis cases. Virchows Arch. 460(1), 103–108 (2012)CrossRefGoogle Scholar
  52. 52.
    Petit, A., Redout, E.M., Ch, V.D.L., de Grauw, J.C., Müller, B., Meyboom, R., et al.: Sustained intra-articular release of celecoxib from in situ forming gels made of acetyl-capped PCLA-PEG-PCLA triblock copolymers in horses. Biomaterials. 53, 426–436 (2015)CrossRefGoogle Scholar
  53. 53.
    Yamazaki, H., Uchiyama, S., Komatsu, M., Hashimoto, S., Kobayashi, Y., Sakurai, T., et al.: Arthroscopic assistance does not improve the functional or radiographic outcome of unstable intra-articular distal radial fractures treated with a volar locking plate: a randomised controlled trial. Bone Joint J. 97-B(7), 957 (2015)CrossRefGoogle Scholar
  54. 54.
    Li, X., Yu, S., Hui, C., Zhu, G., Yuan, L., Qiang, W., et al.: Hydroxycamptothecin induces apoptosis of fibroblasts and prevents intraarticular scar adhesion in rabbits by activating the IRE-1 signal pathway. Eur. J. Pharmacol. 781, 139–147 (2016)CrossRefGoogle Scholar
  55. 55.
    Nishino, K., Omori, G., Koga, Y., Kobayashi, K., Sakamoto, M., Tanabe, Y., et al.: Three-dimensional dynamic analysis of knee joint during gait in medial knee osteoarthritis using loading axis of knee. Gait Posture. 42(2), 127 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information and EngineeringNingbo Dahongying UniversityNingboChina
  2. 2.Tianlin Community Health CenterShanghaiChina
  3. 3.School of EngineeringUniversity of GuelphGuelphCanada

Personalised recommendations