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Articles

Multidomain Feature Level Fusion for Classification of Lumbar Intervertebral Disc Using Spine MR Images

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References

  • S. Bindra, A. G. K. Sinha, and A. L. Benjamin, “Epidemiology of Low Back Pain in Indian Population: A Review,” Int. J. Basic Appl. Med. Sci., Vol. 5, no. 1, pp. 166–79, 2015.
  • D. F. Fardon, “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., Vol. 14, no. 11, pp. 2525–45, 2014. doi: 10.1016/j.spinee.2014.04.022
  • M. Nazeer, S. M. Rao, and R. Simmi Soni, “Lower back pain in South Indians: Causative factors and preventive measures,” Sch. J. App. Med. Sci, Vol. 3, no. 1D, pp. 234–43, 2015.
  • M. T. Modic and J. S. Ross, “Lumbar degenerative disk disease,” Radiology, Vol. 245, no. 1, pp. 43–61, 2007. doi: 10.1148/radiol.2451051706
  • C. W. Pfirrmann, A. Metzdorf, M. Zanetti, J. Hodler, and N. Boos, “Magnetic resonance classification of lumbar intervertebral disc degeneration,” Spine, Vol. 26, no. 17, pp. 1873–8, 2001. doi: 10.1097/00007632-200109010-00011
  • J. Deng, W. Dong, R. Socher, L. Jia Li, K. Li, and L. F. Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009.
  • A. Krizhevsky, I. Sutskever, and G. E. Hington, “Imagenet classification with deep convolution neural networks,” in Conference Proceedings of NIPS, Lake Tahoe, Nevada, USA, 2012, pp. 1106–14.
  • K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016.
  • Q.-S. Sun, S.-G. Zeng, Y. Liu, P.-A. Heng, and D.-S. Xia, “A new method of feature fusion and its application in image recognition,” Pattern Recognit., Vol. 38, no. 12, pp. 2437–48, 2005. doi: 10.1016/j.patcog.2004.12.013
  • M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi, “Fully automatic face normalization and single sample face recognition in unconstrained environments,” Expert. Syst. Appl., Vol. 47, pp. 23–34, April 2016. doi: 10.1016/j.eswa.2015.10.047
  • M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi, “Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition,” IEEE Trans. Inf. Forensics Secur., Vol. 11, no. 9, pp. 1984–96, Sept. 2016. doi: 10.1109/TIFS.2016.2569061
  • A. Kettler and H. J. Wilke, “Review of existing grading systems for cervical or lumbar disc and facet joint degeneration,” Eur. Spine J., Vol. 15, no. 6, pp. 705–18, 2006. doi: 10.1007/s00586-005-0954-y
  • N. Boos, S. Weissbach, H. Rohrbach, C. Weiler, K. F. Spratt, and A. G. Nerlich, “Classification of age-related changes in lumbar intervertebral discs: 2002 Volvo award in basic science,” Spine, Vol. 27, no. 23, pp. 2631–44, 2002. doi: 10.1097/00007632-200212010-00002
  • J. H. Kellgren and J. S. Lawrence, “Rheumatism in miners part II X-ray study,” Br. J. Ind. Med., Vol. 9, no. 3, pp. 197–207, 1952.
  • S. J. Gordon, K. H. Yang, P. J. Mayer, A. H. Mace, V. L. Kish, and E. L. Radin, “Mechanism of disc rupture, a preliminary report,” Spine, Vol. 16, no. 4, pp. 450–6, 1991. doi: 10.1097/00007632-199104000-00011
  • N. E. Lane, M. C. Nevitt, H. K. Genant, and M. C. Hochberg, “Reliability of new indices of radiographic osteoarthritis of the hand and hip and lumbar disc degeneration,” J. Rheumatol., Vol. 20, no. 11, pp. 1911–8, 1993.
  • M. Mimura, M. Panjabi, T. Oxland, J. Crisco, I. Yamamoto, and A. Vasavada, “Disc degeneration affects the multidirectional flexibility of the lumbar spine,” Spine, Vol. 19, no. 12, pp. 1371–80, 1994. doi: 10.1097/00007632-199406000-00011
  • S. S. Madan, A. Rai, and J. M. Harley, “Interobserver error in interpretation of the radiographs for degeneration of the lumbar spine,” Iowa Orthop. J., Vol. 23, pp. 51–6, 2003.
  • J. S. Thalgott, T. J. Albert, A. R. Vaccaro, C. N. Aprill, J. M. Giuffre, J. S. Drake, and J. P. Henke, “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., Vol. 4, no. 6 Suppl, pp. 167S, 2004. doi: 10.1016/j.spinee.2004.07.001
  • H. J. Wilke, F. Rohlmann, C. Neidlinger-Wilke, K. Werner, L. Claes, and A. Kettler, “Validity and interobserver agreement of a new radiographic grading system for intervertebral disc degeneration: part i. lumbar spine,” Eur. Spine J., Vol. 15, no. 6, pp. 720–30, 2006. doi: 10.1007/s00586-005-1029-9
  • G. Schneiderman, B. Flannigan, S. Kingston, J. Thomas, W. H. Dillin, and R. G. Watkins, “Magnetic resonance imaging in the diagnosis of disc degeneration: correlation with discography,” Spine, Vol. 12, no. 3, pp. 276–81, 1987. doi: 10.1097/00007632-198704000-00016
  • D. Butler, J. H. Trafimow, G. B. Andersson, T. W. McNeill, and M. S. Huckman, “Discs degenerate before facets,” Spine, Vol. 15, no. 2, pp. 111–3, 1990. doi: 10.1097/00007632-199002000-00012
  • M. Tertti, H. Paajanen, M. Laato, H. Aho, M. Komu, and M. Kormano, “Disc degeneration in magnetic resonance imaging, a comparative biochemical, histologic, and radiologic study in cadaver spines,” Spine, Vol. 16, no. 6, pp. 629–34, 1991. doi: 10.1097/00007632-199106000-00006
  • R. Gunzburg, R. Parkinson, R. Moore, F. Cantraine, W. Hutton, B. Vernon Roberts, and R. Fraser, “A cadaveric study comparing discography, magnetic resonance imaging, histology, and mechanical behavior of the human lumbar disc,” Spine, Vol. 17, no. 4, pp. 417–26, 1992. doi: 10.1097/00007632-199204000-00007
  • E. P. Southern, M. A. Fye, M. M. Panjabi, P. C. Patel, and J. Cholewicki, “Disc degeneration: A human cadavers study correlating magnetic resonance imaging and quantitative discomanometry,” Spine, Vol. 25, no. 17, pp. 2171–5, 2000. doi: 10.1097/00007632-200009010-00005
  • Z. Askar, D. Wardlaw, T. Muthukumar, F. Smith, D. Kader, and S. Gibson, “Correlation between intervertebral disc morphology and the results in patients undergoing Graf ligament stabilisation,” Eur. Spine J., Vol. 13, no. 8, pp. 714–8, 2004. doi: 10.1007/s00586-004-0702-8
  • J. F. Griffith, Y. X. Wang, G. E. Antonio, K. C. Choi, A. Yu, A. T. Ahuja, and P. C. Leung, “Modified Pfirrmann grading system for lumbar intervertebral disc degeneration,” Spine, Vol. 32, no. 24, pp. E708–12, 2007. doi: 10.1097/BRS.0b013e31815a59a0
  • R. Gunzburg, R. Parkinson, R. Moore, F. Cantraine, W. Hutton, B. Vernon-Roberts, and R. Fraser, “A cadaveric study comparing discography, magnetic resonance imaging, histology, and mechanical behavior of the human lumbar disc,” Spine, Vol. 17, no. 4, pp. 417–26, 1992. doi: 10.1097/00007632-199204000-00007
  • U. Berlemann, N. C. Gries, and R. J. Moore, “The relationship between height, shape and histological changes in early degeneration of the lower lumbar discs,” Eur. Spine J., Vol. 7, no. 3, pp. 212–7, 1998. doi: 10.1007/s005860050058
  • C. Weiler, M. Lopez-Ramos, H. M. Mayer, A. Korge, C. J. Siepe, K. Wuertz, V. Weiler, N. Boos, and A. G. Nerlich, “Histological analysis of surgical lumbar intervertebral disc tissue provides evidence for an association between disc degeneration and increased body mass index,” BMC. Res. Notes., article no. 497, 2011.
  • J. J. Corso, R. S. Alomari, and V. Chaudhary, “Lumbar disc localization and labeling with a probabilistic model on both pixel and object features,” Conf Proc MICCAI Med Image Comput Comput Assist Interv, Vol. 11, no. Pt 1, pp. 202–10, 2008.
  • R. S. Alomari, J. J. Corso, and V. Chaudhary, “Abnormality detection in lumbar discs from clinical MR images with a probabilistic model,” in Proceedings of 23rd International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2009), Berlin, Germany, 2009.
  • R. S. Alomari, J. J. Corso, V. Chaudhari, and G. Dhillon, “Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model,” in Conference Proceedings in Biomedical Imaging: From Nano to Macro, Bosten, USA, 2009, pp. 546–9.
  • R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon, “Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI,” Int. J. Comput. Assist. Radiol. Surg., Vol. 5, no. 3, pp. 287–93, 2010. doi: 10.1007/s11548-009-0396-9
  • A. Watanabe, L. Benneker, C. Boesch, T. Obata, S. Anderson, and T. Watanabe, “Classification of intervertebral disk degeneration with axial T2 mapping,” AJR Am. J. Roentgenol., Vol. 189, no. 4, pp. 936–42, 2007. doi: 10.2214/AJR.07.2142
  • E. P. Southern, M. A. Fye, M. M. Panjabi, T. C. Patel, and J. Cholewicki, “Disc degeneration: a human cadaveric study correlating magnetic resonance imaging and quantitative discomanometry,” Spine, Vol. 25, no. 17, pp. 2171–5, 2000. doi: 10.1097/00007632-200009010-00005
  • M. da Silva Barreiro, H. Marcello, and R. Rangayyan, “Semiautomatic classification of intervertebral disc degeneration in magnetic resonance images of the spine,” in Conference Proceedings of 5th Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, Salvador, Brazil, 2014, pp. 1–5.
  • I. Castro-Mateos, R. Hua, J. M. Pozo, A. Lazary, and A. F. Frangi, “Intervertebral disc classification by its degree of degeneration from T2 weighted magnetic resonance images,” Eur. Spine J., Vol. 25, no. 9, pp. 2721–7, 2016. doi: 10.1007/s00586-016-4654-6
  • G. Litjens, et al., “A survey on deep learning in medical image analysis,” Med. Image Anal., Vol. 42, pp. 60–88, 2017. doi: 10.1016/j.media.2017.07.005
  • O. Ronnerberger, P. Fischer, and T. Brox, “U-Net: Convolution networks for biomedical image segmentation,” in Conference Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015, Vol. 9351, pp. 234–41 .
  • W. Shen, F. Yang, W. Mu, C. Yang, X. Yang, and J. Tian, “Automatic localization of vertebrae based on convolutional neural networks,” in Conference Proceedings of SPIE 9413, Medical Imaging 2015: Image Processing, Orlando, Florida, US, 2015, pp. 94132E-1–6.
  • H. Chen, C. Shen, J. Qin, D. Ni, L. Shi, C. Y. Jack, and P.-A. Heng, “Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks,” in Conference Proceedings Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015, Vol. 9349, pp. 515–22.
  • A. Suzani, A. Rasoulian, A. Seitel, S. Fels, R. Rohling, and P. Abolmaesumi, “Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MRimages,” in Conference Proceedings of SPIE Medical Imaging, Orlando, Florida, US, 2015, Vol. 9415, pp. 9415141–7 .
  • Y. Cai, M. Landis, D. T. Laidley, A. Kornecki, A. Lum, and S. Li, “Multi-modal vertebrae recognition using transformed deep convolution network,” Comput. Med. Imaging Graph, Vol. 51, pp. 11–9, 2016. doi: 10.1016/j.compmedimag.2016.02.002
  • R. Korez, B. Likar, F. Pernuš, and T. Vrtovec, “Model-based segmentation of vertebral bodies from MR images with 3D CNNs,” in Conference Proceedings Medical Image Computing and Computer-Assisted Intervention, Athens, Greece, Vol. 9901, pp. 433–41, 2016 .
  • A. Jamaludin, T. Kadir, and A. Zisserman, “Spinenet: Automated classification and evidence visualization in spinal MRIs,” Med. Image Anal., Vol. 41, pp. 63–73, 2017. doi: 10.1016/j.media.2017.07.002
  • D. Forsberg, E. Sjöblom, and J. L. Sunshine, “Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data,” J. Digit Imaging, Vol. 30, no. 4, pp. 406–12, 2017. doi: 10.1007/s10278-017-9945-x
  • Serial or concate C. J. Liu and H. Wechsler, “A shape- and texture based enhanced fisher classifier for face recognition,” IEEE Trans. Image Process, Vol. 10, no. 4, pp. 598–608, 2001. doi: 10.1109/83.913594
  • J. Yang, J.-Y. Yang, D. Zhang, and J.-F. Lu, “Feature fusion: Parallel strategy vs. serial strategy,” Pattern Recognit., Vol. 36, no. 6, pp. 1369–81, 2003. doi: 10.1016/S0031-3203(02)00262-5
  • T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE T Patt Anal Mach Intell, Vol. 24, pp. 971–87, 2002. doi: 10.1109/TPAMI.2002.1017623
  • Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, Vol. 521, no. 7553, pp. 436–44, 2015. doi: 10.1038/nature14539
  • C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., Vol. 20, no. 3, pp. 273–97, 1995.
  • I. Jolliffe. Principal Component Analysis. International Encyclopedia of Statistical Science. Berlin: Springer, 2011.
  • K. I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” IEEE Trans Pattern Anal. Mach. Intell., Vol. 32, no. 6, pp. 1127–33, 2010. doi: 10.1109/TPAMI.2010.25
  • P. N. Belhumeur, J. P. Hespanha, and D. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 19, no. 7, pp. 711–20, 1997. doi: 10.1109/34.598228

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