CBIR-DSN: integrating clustering and retrieval platforms for disk space narrowing degradation assessment

  • Aouache MustaphaEmail author
  • Aini Hussain
  • Wan Siti Halimatul Munirah Wan Ahmad
  • Wan Mimi Diyana Wan Zaki
  • Hamzaini Bin Abdul Hamid


A system that is capable of assessing spine osteoarthritis conditions which affect a significant portion of the elderly population could be very valuable to radiologists, researchers of arthritis and musculoskeletal diseases, and educators. To this end, there is very limited research published in the literature concerning the degradation assessment of spinal intervertebral disc space narrowing (DSN). Thus, this paper intends to develop a system that focuses on assessing the degradation of disk space narrowing (DSN) to assist in radiologist’s decision-making in the characterization of cervical and lumbar images. A novel experiment based on our previous research (Aouache et al. 2009; Aouache et al. Biomed Eng Online 14(1):6, 2015) was conducted by integrating clustering and retrieval platforms to achieve this objective. Two shape boundary, 9-points, and B-spline have been used as the foundation for DSN model construction using active shape model. The segmented DSNs have then indexed via region and contour-based features descriptor. For better efficiency, clustering using a vocabulary tree model (VTM) is constructed to identify correct DSN cluster and build multi-clusters subsets for faster and robust retrieval research process. Our system achieved an accuracy of average retrieval rate (ARR) more than 90% and 88% for cervical and lumbar data set accordingly. We expect the proposed system will assist in decision-making and uses by radiologists or researchers for further investigation.


Spine radiography Disk space narrowing Modeling Indexing approach VTM clustering DSN retrieval 



This work is supported in parts by the Ministry of Science, Technology, and Innovation and Centre for Integrated Systems Engineering and Advanced Technologies (INTEGRA), Universiti Kebangsaan Malaysia (project code: DIP-2018- 020) along with the collaboration and participation of SIA research team, Division Telecom, CDTA, Algeria.


  1. 1.
    Akgul CB, Rubin DL, Napel S, Beaulieu CF, Greenspan H, Acar B (2011) Content based image retrieval in radiology: current status and future directions. J Digit Imaging 24(2):208–222Google Scholar
  2. 2.
    Antani S, Kasturi R, Jain R (2002) A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognit 35(4):945–965zbMATHGoogle Scholar
  3. 3.
    Aouache M, Aini H, Abdul SS, Zulkifley MA (2014) Toward underspecified queries enhancement using retrieval and classification platforms. In: 2014 IEEE symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP), pp 1–7Google Scholar
  4. 4.
    Aouache M, Hussain A, Samad SA (2011) A new approach for noise reduction in spine radiograph images using a non-linear contrast adjustment scheme based adaptive factor. Sci Res Essays 6(20):4246–4258Google Scholar
  5. 5.
    Aouache M, Hussain A, Samad SA, Hamid HA, Ariffin AK (2008) Osteoporosis presence verification using mace filter based statistical models of appearance with application to cervical X-ray images. In: 4th Kuala Lumpur international conference on biomedical engineering. Springer, Berlin, pp 607–610Google Scholar
  6. 6.
    Aouache M, Hussain A, Samad SA, Hamid HA, Ariffin AK (2009) Automatic vertebral fracture assessment system (AVFAS) for spinal pathologies diagnosis based on radiograph x-ray images. In: International visual informatics conference. Springer, Berlin, pp 122–135Google Scholar
  7. 7.
    Aouache M, Hussain A, Samad SA, Zulkiey MA, Zaki WMDW, Hamid HA (2015) Design and development of a content-based medical image retrieval system for spine vertebrae irregularity. Biomed Eng Online 14(1):6Google Scholar
  8. 8.
    Aouache M, Oulefki A, Bengherabi M, Boutellaa E, Algaet MA (2017) Towards nonuniform illumination face enhancement via adaptive contrast stretching. Multimed Tools Appl:1–39Google Scholar
  9. 9.
    Aouache MM, Hussain A, Abdul Samad SA, Kamal WAA, Hamid HA (2007) Active shape modeling of medical images for vertebral fracture computer assisted assessment system. In: 5th student conference on research and development SCOReD. IEEE, pp 1–6Google Scholar
  10. 10.
    Aouache MM, Hussain A, Zulkifley MA, Zaki WMDW, Hamid HA (2018) Anterior osteoporosis classification in cervical vertebrae using fuzzy decision tree. Multimed Tools Appl 77:4011Google Scholar
  11. 11.
    Arebey M, Hannan MA, Begum RA, Basri H (2012) Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach. J Environ Manag (104):9–18Google Scholar
  12. 12.
    AyuniMohd IIi, Hussain A, Zulkifley MA, Md N, Tahir M, Aouache M (2014) An analysis of x-ray image enhancement methods for vertebral bone segmentation. In: 10th international colloquium on IEEE signal processing and its applications (CSPA), pp 208–211Google Scholar
  13. 13.
    AyuniMohd II, Zulkifley MA, Hussain A, Aouache M (2015) Automated vertebrae extraction using watershed segmentation and tree-based modelling approach. J Fiber Bioeng Inform 8(3):547–555Google Scholar
  14. 14.
    Baldi A, Murace R, Dragonetti E, Manganaro M, Guerra O, Bizzi S, Galli L (2009) Definition of an automated content-based image retrieval (cbir) system for the comparison of dermoscopic images of pigmented skin lesions. Biomed Eng Online 8(1):18Google Scholar
  15. 15.
    Brown CD, Davis HT (2006) Receiver operating characteristics curves and related decision measures: a tutorial. Chemom Intell Lab Syst 80(1):24–38Google Scholar
  16. 16.
    Charles E, Kahn J, Thao C (2007) Goldminer: a radiology image search engine. Am J Roentgenol 188(6):1475–1478Google Scholar
  17. 17.
    Chung C-T, Tsai S-W, Chen C-J, Wu T-C, Wang D, Lan H-CH, Wu S-K (2009) Comparison of the intervertebral disc spaces between axial and anterior lean cervical traction. Eur Spine J 18(11):1669–1676Google Scholar
  18. 18.
    Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59Google Scholar
  19. 19.
    Fernand M (1994) Topographic distance and watershed lines. Signal Process 38 (1):113–125zbMATHGoogle Scholar
  20. 20.
    Frobin W, Leivseth G, Biggemann M, Brinckmann P (2002) Vertebral height, disc height, posteroanterior displacement and dens-atlas gap in the cervical spine: precision measurement protocol and normal data. Clin Biomech 17(6):423–431Google Scholar
  21. 21.
    Hamalainen O, Vanharanta H, Kuusela T (1993) Degeneration of cervical intervertebral disks in fighter pilots frequently exposed to high+ gz forces. Aviat Space Environ Med 64(8):692–696Google Scholar
  22. 22.
    Haralick RM, Shanmugam K, et al. (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621Google Scholar
  23. 23.
    Jian W, Sun X, Luo S (2012) Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform. Biomed Eng Online 11(1):96Google Scholar
  24. 24.
    Jye LD, Antani S, Chang Y, Gledhill K, Long LR, Christensen P (2009) CBIR Of spine X-ray images on inter-vertebral disc space and shape profiles using feature ranking and voting consensus. Data Knowl Eng 68(12):1359–1369Google Scholar
  25. 25.
    Kalifa G, Cohen PA, Hamidou A (2002) The intervertebral disk: a landmark for spinal diseases in children. Eur Radiol 12(3):660–665Google Scholar
  26. 26.
    Kauppinen H, Seppanen T, Pietikainen M (1995) An experimental comparison of auto regressive and fourier-based descriptors in 2d shape classification. IEEE Trans Pattern Anal Mach Intell 17(2):201– 207Google Scholar
  27. 27.
    Kettler A, Rohlmann F, Neidlinger-Wilke C, Werner K, Claes L, Wilke H-J (2006) Validity and interobserver agreement of a new radiographic grading system for intervertebral disc degeneration: Part ii. cervical spine. European Spine J 15 (6):732–741Google Scholar
  28. 28.
    Kumar A, Kim J, Cai W, Fulham M, Feng D (2013) Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J Digit Imaging 26(6):1025–1039Google Scholar
  29. 29.
    Kuo W-J, Chang R-F, Lee CC, Moon WK, Chen D-R (2002) Retrieval technique for the diagnosis of solid breast tumors on sonogram. Ultrasound Med Biol 28(7):903–909Google Scholar
  30. 30.
    Lee D-J, Antani S, Chang Y, Gledhill K, Long LR, Christensen P (2009) Cbir of spine x-ray images on inter-vertebral disc space and shape profiles using feature ranking and voting consensus. Data Knowl Eng 68(12):1359–1369Google Scholar
  31. 31.
    Lehmann TM, Wein BB, Dahmen J, Bredno J, Vogelsang F, Kohnen M (1999) Content based image retrieval in medical applications a novel multi step approach. In: Storage and retrieval for media database, international society for optics and photonics, vol 188, pp 312–321Google Scholar
  32. 32.
    Ling C, Diyana WM, Zaki W, Hussain A, Ahmad SHMW, Hing EY (2017) Shape based image retrieval system for mri spine, 6th International Conference on. IEEE, pp 1–6Google Scholar
  33. 33.
    Ling C, Diyana WM, Zaki W, Hussain A, Hamid HA (2016) Semi-automated vertebral segmentation of human spine in mri images. In: International conference on advances in electrical, electronic and systems engineering (ICAEES). IEEE, pp 120–124Google Scholar
  34. 34.
    Long LR, Pillemer SR, Lawrence RC, Goh G-H, Neve L, Thoma GR (1998) WebMIRS: web-based medical information retrieval system. In: Storage and retrieval for image and video databases (SPIE), pp 392–403Google Scholar
  35. 35.
    Lu J, Ebraheim NA, Huntoon M, Haman SP (2000) Cervical intervertebral disc space narrowing and size of intervertebral foramina. Clin Orthop Relat Res 370:259–264Google Scholar
  36. 36.
    Miller J, Schmatz C, Schultz A (1988) Lumbar disc degeneration: correlation with age, sex, and spine level in 600 autopsy specimens. Spine 13(2):173–178Google Scholar
  37. 37.
    Naghdy G, Wang J, Ogunbona P (1996) Texture analysis using Gabor wavelets: 74Google Scholar
  38. 38.
    Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree. In: 2006 IEEE computer society conference on computer vision and pattern recognition (2), pp 2161–2168Google Scholar
  39. 39.
    Paajanen H, Erkintalo M, Parkkola R, Salminen J, Kormano M (1997) Age-dependent correlation of low-back pain and lumbar disc degeneration. Arch Orthop Trauma Surg 116(1):106–107Google Scholar
  40. 40.
    Parsons JR, Lee CK, Langrana NA, Clemow AJ, Chen EH, Hawkins MV (1996) Functional and biocompatible intervertebral disc spacer containing elastomeric material of varying hardness, U.S. Patent No. 5,545,229. washington, DC: U.S Patent and Trademark Office.Google Scholar
  41. 41.
    Qian X, Tagare HD, Fulbright RK, Long R, Antani S (2010) Optimal embedding for shape indexing in medical image databases. Med Image Anal 14(3):243–254Google Scholar
  42. 42.
    Shokr ME (1991) Evaluation of second-order texture parameters for sea ice classification from radar images. J Geophys Res Oceans 96(C6):10625–10640Google Scholar
  43. 43.
    Shyu CR, Brodley CE, Kak AC, Kosaka A, Aisen AM, Broderick LS (1999) Assert: a physician-in-the-loop content-based retrieval system for hrct image databases. Comput Vis Image Underst 75(1-2):111–132Google Scholar
  44. 44.
    Tang LH, Hanka R, Ip Horace HS (1999) A review of intelligent content-based indexing and browsing of medical images. Health Informatics J 5(1):40–49Google Scholar
  45. 45.
    Thoma GR, Long LR, Antani S (2002) Content-based image retrieval (cbir) of biomedical images Report to the NLM/LHC Board of Scientific CounselorsGoogle Scholar
  46. 46.
    Wang JZ (2000) Pathfinder: multiresolution region-based searching of pathology images using IRM. In: Proceedings of the AMIA symposium, american medical informatics association, p 883Google Scholar
  47. 47.
    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):720–730Google Scholar
  48. 48.
    Xiangyuan L, Ma AJ, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826–5841MathSciNetGoogle Scholar
  49. 49.
    Xiangyuan L, Ye M, Zhang S, Yuen PC (2018) Robust collaborative discriminative learning for RGB-infrared tracking. In: AAAIGoogle Scholar
  50. 50.
    Xiangyuan L, Yuen PC, Chellappa R (2017) Robust MIL-based feature template learning for object tracking. In: AAAI, pp 4118–4125Google Scholar
  51. 51.
    Xiangyuan L, Yuen S, Zhang PC (2016) Robust joint discriminative feature learning for visual tracking. In: IJCAI, pp 3403–3410Google Scholar
  52. 52.
    Xiangyuan L, Zhang S, Yuen PC, Chellappa R (2018) Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Trans Image Process 27(4):2022–2037MathSciNetGoogle Scholar
  53. 53.
    Xu X, Lee D-J, Antani S, Long LR (2008) A spine x-ray image retrieval system using partial shape matching. IEEE Trans Inf Technol Biomed 12(1):100–108Google Scholar
  54. 54.
    Yadav RB, Nishchal NK, Gupta AK, Rastogi VK (2007) Retrieval and classification of shape-based objects using fourier, generic fourier, and wavelet-fourier descriptors technique: a comparative study. Opt Lasers Eng 45(6):695–708Google Scholar
  55. 55.
    Yuan L, Wang Y, Thompson PM, Narayan VA, Ye J (2012) Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1149–1157Google Scholar
  56. 56.
    Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recognit 37(1):1–9Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division TélécomCentre de Développement des Technologies Avancées (CDTA)Baba HassenAlgeria
  2. 2.Centre for Integrated Systems Engineering and Advanced Technologies (INTEGRA), Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaSelangorMalaysia
  3. 3.Department of Radiology, Faculty of MedicineUniversiti Kebangsaan MalaysiaSelangorMalaysia

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