Unsupervised Lumbar IVD Localization and Segmentation using GFMM and Boundary Refined Region Growing Techniques

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : S. Shirly, R. Golden Nancy, R. Venkatesan, T. Jemima Jebaseeli, K. Ramalakshmi
  10.14445/22315381/IJETT-V70I4P218

MLA 

MLA Style: Shirly, S., et al. "Unsupervised Lumbar IVD Localization and Segmentation using GFMM and Boundary Refined Region Growing Techniques." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 215-222. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P218

APA Style: Shirly, S., Golden Nancy, R., Venkatesan, R., Jemima Jebaseeli, T., Ramalakshmi, K. (2022). Unsupervised Lumbar IVD Localization and Segmentation using GFMM and Boundary Refined Region Growing Techniques. International Journal of Engineering Trends and Technology, 70(4), 215-222. https://doi.org/10.14445/22315381/IJETT-V70I4P218

Abstract
Low Back Pain is caused because of Lumbar Intervertebral Disc (IVD) degeneration, and it is one of the most suffered problems by a large population. in this paper, the lumbar IVD is automatically localized and segmented using Gabor Filter with Mathematical Morphology and novel Boundary Refined Region Growing techniques, respectively. an MRI dataset is used to validate the suggested approach, consisting of 180 IVDs from 30 subjects. Initially, the Gabor Filter with Mathematical Morphology and Support Vector Machine with Local Binary Pattern techniques are used in localizing the lumbar IVD. in comparison to performance, Gabor Filter with Mathematical Morphology localized with 100% accuracy. In contrast, SVM localized with 89.4% for the precision range of 2mm. the Gabor Filter with Mathematical Morphology attained an accuracy of 96.9% for the 0.6mm precision range, which is comparatively higher than the accuracy of SVM for the 2mm precision range. Then the segmentation is preceded by the novel Boundary Refined Region Growing technique on the lumbar IVD image localized by Gabor Filter with Mathematical Morphology, achieving a better Dice Similarity Index, sensitivity, and specificity of 86.2%, 92%, and 99%, respectively.

Keywords
Lumbar Intervertebral Disc, Magnetic Resonance Imaging, Gabor filter, Mathematical Morphology, Support vector machine, Boundary refined region growing.

Reference
[1] Modic, M.T, Ross., Lumbar Degenerative Disk Disease, Radiology, 245(1) (2007) 43-61.
[2] An H.S., Introduction: Disc Degeneration: Summary. Spine, 29(23) (2004) 2677-2678.
[3] Castro-Mateos I., 2D Segmentation of Intervertebral Discs and its Degree of Degeneration from T2-Weighted Magnetic Resonance Images, SPIE Medical Imaging, International Society for Optics and Photonics, (2014).
[4] Michopoulou S.K., Atlas-Based Segmentation of Degenerated Lumbar Intervertebral Discs from M.R. Images of the Spine, IEEE Transactions on Biomedical Engineering, 56(9) (2019) 2225-2231.
[5] Schmidt Et Al., Spine Detection and Labelling using A Parts-Based Graphical Model, Biennial International Conference on Information Processing in Medical Imaging, Springer, (2017).
[6] Zhu X Et Al., A Method of Localization and Segmentation of Intervertebral Discs in Spine MRI Based on Gabor Filter Bank, Biomedical Engineering Online, 15(1) (2016).
[7] Chevrefils C Et Al., Watershed Segmentation of Intervertebral Disk and the Spinal Canal from MRI Images, International Conference Image Analysis and Recognition, Springer, (2007).
[8] Oktay A.B. & Y.S. Akgul., Simultaneous Localization of Lumbar Vertebrae and Intervertebral Discs with SVM-Based MRF, IEEE Transactions on Biomedical Engineering, 60(9) (2013) 2375-2383.
[9] Raja A, J.J. Corso & V. Chaudhary., Labelling Lumbar Discs using Both Pixel-and Object-Level Features with a Two-Level Probabilistic Model, IEEE Transactions on Medical Imaging, 30(1) (2011) 1-10.
[10] Peng Z Et Al., Automated Vertebra Detection and Segmentation from the Whole Spine M.R. Images. in Engineering in Medicine and Biology Society,(2005).
[11] Shi R Et Al., An Efficient Method for Segmentation of MRI Spine Images, Complex Medical Engineering,(2007).
[12] Haq R Et Al., 3D Lumbar Spine Intervertebral Disc Segmentation and Compression Simulation From MRI using Shape-Aware Models, International Journal of Computer-Assisted Radiology and Surgery, 10(1) (2015) 45-54.
[13] Vukadinovic D & M. Pantic., Fully Automatic Facial Feature Point Detection using Gabor Feature Based Boosted Classifiers. in Systems, Man and Cybernetics,(2005)
[14] Kaya M & O. Bebek., Needle Localization using Gabor Filtering in 2D Ultrasound Images. in Robotics and Automation (ICRA), (2014).
[15] Hose J Et Al., Precise Localization of Landmarks on 3D Faces using Gabor Wavelets, Biometrics: Theory, Applications, and Systems, (2007).
[16] Kumar A & G.K. Pang., Defect Detection in Textured Materials using Gabor Filters, IEEE Transactions on Industry Applications, 38(2) (2002) 425-440.
[17] Oktay A.B. & Y.S. Akgul., Localization of the Lumbar Discs using Machine Learning and Exact Probabilistic Inference, International Conference on Medical Image Computing and Computer-Assisted Intervention, (2011).
[18] Ghosh S Et Al., A New Approach to Automatic Disc Localization in Clinical Lumbar MRI: Combining Machine Learning with Heuristics. in Biomedical Imaging (ISBI), (2012).
[19] Kamarainen J.K., V. Kyrki, & H. Kalviainen. , Invariance Properties of Gabor Filter-Based Features-Overview and Applications, IEEE Transactions on Image Processing, 15(5) (2006) 1088-1099.
[20] Lee T.S., Image Representation using 2D Gabor Wavelets, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10) (1996) 959-971.
[21] Daugman J.G., Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression, IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(7) (1988) 1169-1179.
[22] Haralick R.M, S.R. Sternberg & X. Zhuang., Image Analysis using Mathematical Morphology, IEEE Transactions on Pattern Analysis and Machine Intelligence, 4 (1987) 532-550.
[23] Guo Z, L. Zhang & D. Zhang., Completed Modelling of Local Binary Pattern Operator for Texture Classification, IEEE Transactions on Image Processing, 19(6) (2010) 1657-1663.
[24] Adankon M.M. & M. Cheriet., Support Vector Machine, Encyclopedia of Biometrics, (2009) 1303-1308.