Intervertebral Disc Classification Using Deep Learning Technique

  • J. V. ShindeEmail author
  • Y. V. Joshi
  • R. R. Manthalkar
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


This paper describes the semiautomatic method for diagnosis of intervertebral disc degeneration according to Pfirrmann’s five scale (1–5) grading system, which is used in the assessment of disc degeneration severity. Total 1123 discs are obtained after augmentation from 120 subject’s T2-weighted lumbar scans. Manual classification into five grades is done by experts. Our method is extracting 59 features using Local Binary Pattern for texture analysis and 4096 features using pretrained CNN. 1 × 59 and 1 × 4096 feature vectors are fused to form 1 × 4155 feature vector to train our multiclass Support Vector Machine classifier. This feature level fusion method is able to achieve 80.40% accuracy. A Quantitative analysis is done using parameters, viz.,—Accuracy, Sensitivity, Specificity, Precision, Recall, F1 score, etc.


Deep learning Intervertebral disc degeneration Classification 



We thank Dr. Hemant Borse, Consultant Radiologist, Samarth Diagnostic Center, Nasik and Dr. Rajesh Jawale, Consultant Radiologist, Wockhardt Hospital Nasik (M.S.) India who provided insight and expertise that greatly assisted the research. We are immensely grateful to Dr. Hemant Borse and his technical team for providing spine MR image dataset for research work.


  1. 1.
    Bindra S, Sinha AGK, Benjamin AI (2015) Epidemiology of low back pain in indian population: a review. Int J Basic Appl Med Sci 5(1):166–179. ISSN 2277–2103 (Online) 015Google Scholar
  2. 2.
    Fardon DF (2014) Lumbar disc nomenclature: version 2.0 Recommendations of the combined task forces of the North American Spine Society, the American Society of Spine Radiology and the American Society of Neuroradiology Review Article. Spine J 14:2525–2545Google Scholar
  3. 3.
    Nazeer M, Rao SM, Soni S, Ravinder M (2015) Lower back pain in South Indians: causative factors and preventive measures. Sch J App Med Sci 3(1D):234–243. ISSN 2347-954XGoogle Scholar
  4. 4.
    Modic MT, Ross JS (2007) Lumbar degenerative disk disease. Radiology 245:43–61CrossRefGoogle Scholar
  5. 5.
    Pfirrmann CWA, Metzdorf A, Zanetti M, Hodler J (2001) Magnetic resonance classification of lumbar intervertebral degeneration. SPINE 26:1873–1878CrossRefGoogle Scholar
  6. 6.
    Ketler A, Wilke HJ (2006) Review of existing grading systems for cervical or lumbar disc and facet joint degeneration. Eur Spine J 15:705–718CrossRefGoogle Scholar
  7. 7.
    Bennekar LM, Heini PF, Anderson SE (2005) Correlation of radiographic and MRI parameters to morphological and biochemical assessment of intervertebral disc degeneration. Eur Spine J 14:27–35CrossRefGoogle Scholar
  8. 8.
    Kellgren JH, Lawrence JS (1952) Rheumatism in miners Part II X-ray study. Br J Ind Med 9(3):197–207Google Scholar
  9. 9.
    Gordon SJ, Yang KH, Mayer PJ, Mace AH Jr, Kish VL, Radin EL (1991) Mechanism of disc rupture. A preliminary report Spine (Phila Pa 1976) 16(4):450–456CrossRefGoogle Scholar
  10. 10.
    Lane NE, Nevitt MC, Genant HK, Hochberg MC (1993) Reliability of new indices of radiographic osteoarthritis of the hand and hip and lumbar disc degeneration. J Rheumatol 20(11):1911–1918Google Scholar
  11. 11.
    Mimura M, Panjabi M, Oxland T, Crisco J, Yamamoto I, Vasavada A (1994) Disc degeneration affects the multidirectional flexibility of the lumbar spine. Spine (Phila Pa 1976) 19(12):1371–1380CrossRefGoogle Scholar
  12. 12.
    Madan SS, Rai A, Harley JM (2003) Interobserver error in interpretation of the radiographs for degeneration of the lumbar spine. Iowa Orthop J 23:51–56Google Scholar
  13. 13.
    Thalgott J, Albert T, Vaccaro A, et al (2004) A new classification system for degenerative disc disease of the lumbar spine based on magnetic resonance imaging, provocative discography, plain radiographs and anatomic considerations. Spine J 4(6 Suppl):S167CrossRefGoogle Scholar
  14. 14.
    Wilke HJ, Rohlmann F, Neidlinger-Wilke C, Werner K, Claes L, Kettler A (2006) Validity and interobserver agreement of a new radiographic grading system for intervertebral disc degeneration: Part I. Lumbar spine. Eur Spine J 15(6):720–730CrossRefGoogle Scholar
  15. 15.
    Schneiderman G, Flannigan B, Kingston S, Thomas J, Dillin WH, Watkins RG (1987) Magnetic resonance imaging in the diagnosis of disc degeneration: correlation with discography. Spine (Phila Pa 1976) 12(3):276–281CrossRefGoogle Scholar
  16. 16.
    Butler D, Trafimow JH, Andersson GB, McNeill TW, Huckman MS (1990) Discs degenerate before facets. Spine (Phila Pa 1976) 15(2):111–113CrossRefGoogle Scholar
  17. 17.
    Tertti M, Paajanen H, Laato M, Aho H, Komu M, Kormano M (1991) Disc degeneration in magnetic resonance imaging. A comparative biochemical, histologic, and radiologic study in cadaver spines. Spine (Phila Pa 1976) 16(6):629–634CrossRefGoogle Scholar
  18. 18.
    Gunzburg R, Parkinson R, Moore R, Cantraine F, Hutton W, Vernon Roberts B, Fraser R (1992) A cadaveric study comparing discography, magnetic resonance imaging, histology, and mechanical behavior of the human lumbar disc. Spine 17(4):417–426CrossRefGoogle Scholar
  19. 19.
    Southern EP, Fye MA, Panjabi MM, Patel TC, Cholewicki J (2000) Disc degeneration: a human cadaveric study correlating magnetic resonance imaging and quantitative discomanometry. Spine 25(17):2171–2175CrossRefGoogle Scholar
  20. 20.
    Askar Z, Wardlaw D, Muthukumar T, Smith F, Kader D, Gibson S (2004) Correlation between intervertebral disc morphology and the results in patients undergoing Graf ligament stabilisation. Eur Spine J 13(8):714–718CrossRefGoogle Scholar
  21. 21.
    Griffith JF, Wang YX, Antonio GE, Choi KC, Yu A, Ahuja AT, Leung PC (2007) Modified Pfirrmann grading system for lumbar intervertebral disc degeneration. Spine (Phila Pa 1976) 32(24):E708-E712CrossRefGoogle Scholar
  22. 22.
    Boos N, Weissbach S, Rohrbach H, Weiler C, Spratt KF, Nerlich AG. Classification of age-related changes in lumbar intervertebral discs: 2002 volvo award in basic science. Spine. 2002; 27(23):2631-44. CrossRefGoogle Scholar
  23. 23.
    Berlemann U, Gries NC, Moore RJ (1998) The relationship between height, shape and histological changes in early degeneration of the lower lumbar discs. Eur Spine J 7(3):212-217CrossRefGoogle Scholar
  24. 24.
    Weiler C, Lopez-Ramos M, Mayer HM, Korge A, Siepe CJ, Wuertz K, Weiler V, Boos N, Nerlich AG (2011) Histological analysis of surgical lumbar intervertebral disc tissue provides evidence for an association between disc degeneration and increased body mass index. BMC Res Notes 4(1):497CrossRefGoogle Scholar
  25. 25.
    Corso JJ, Alomari RS, Chaudhary V (2008) Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Proceeding of MICCAI, vol 5241 of LNCS Part 1. Springer, pp 202–210Google Scholar
  26. 26.
    Alomari RS, Corso JJ, Chaudhary V (2009) Abnormality detection in lumbar discs from clinical MR images with a probabilistic model. In: Proceeding of CARSGoogle Scholar
  27. 27.
    Alomari RS, Corso JJ, Chaudhari V, Dhillon G (2009) Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model. In: Proceeding of ISBI’09, pp 546–549Google Scholar
  28. 28.
    Alomari RS, Corso JJ, Chaudhary V, Dhillon G (2009) Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI. Int J Comput Assist Radiol Surg 5(3):287–293CrossRefGoogle Scholar
  29. 29.
    Watanabe A, Benneker L, Boesch C, Obata T, Anderson S, Watanabe T (2007) Classification of intervertebral disk degeneration with axial T2 mapping. AJR 189(4):936–942CrossRefGoogle Scholar
  30. 30.
    da Silva Barreiro M, Marcello H, Rangayyan R (2014) Semiautomatic classification of intervertebral disc degeneration in magnetic resonance images of the spine. In: 5th ISSNII-IEEE Biosignals and biorobotics for better and safer living (BRC) conference, pp 1–5Google Scholar
  31. 31.
    Castro-Mateos I, Hua R, Pozo JM, Lazary A, Frangi AF (2016) Intervertebral disc classification by its degree of degeneration from T2 weighted magnetic resonance images. Eur Spine J 25(9):2721–2727CrossRefGoogle Scholar
  32. 32.
    Ronnerberger O, Fischer P, Brox T (2015) U-net: convolution networks for biomedical image segmentation. In: MICCAI, Springer, LNCS, vol 9351, pp 234–241Google Scholar
  33. 33.
    Jamaludin Amir, Kadir Timor, Zisserman Andrew (2017) SpineNet: automated classification and evidence visualization in spinal MRIs. Med Image Anal 41:63–73CrossRefGoogle Scholar
  34. 34.
    Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 32(6):1127–1133CrossRefGoogle Scholar
  35. 35.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation Invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987CrossRefGoogle Scholar
  36. 36.
    Krizhevsky A, Sutskever I, Hington GE (2012) ImageNet classification with deep convolution neural networks. In: NIPS, pp 1106–1114Google Scholar
  37. 37.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • J. V. Shinde
    • 1
    Email author
  • Y. V. Joshi
    • 2
  • R. R. Manthalkar
    • 2
  1. 1.Department of Computer EngineeringL.G.N. Sapkal College of EngineeringNasikIndia
  2. 2.Department of Electronics & TelecommunicationS.G.G.S Institute of Engineering and TechnologyNandedIndia

Personalised recommendations