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Warianty tytułu
Języki publikacji
Abstrakty
One of the vital organs, which manage the communication between the brain and different body parts, is the spinal cord. It is highly prone to the traumatic injuries and to several diseases. The vital criteria for the clinical management are the appropriate localization and segmentation of the spinal cord. The segmentation experiences the risks, associated with the diversity in the human anatomy and contrast variation inMagnetic Resonance Imaging (MRI). Hence, an efficacious segmentation method must be devised for the effective segmentation and disc localization of the spinal cord. Correspondingly, the here contained survey provides the review of the distinct segmentation schemes for the spinal cord segmentation. At present, there is an urgent requirement for the development of an effective segmentation approach so as to outperform the existing segmentation methods. In this research article, a detailed survey on several research works presenting the recommended segmentation schemes, based on the active contour model, semi-automated segmentation, deformable model, probabilistic model, graph-based segmentation, and so on, is presented. Additionally, an in depth analysis and discussion are provided, in accordance with the publication year, evaluation metrics, segmentation scheme, Magnetic Resonance (MR) image datasets, Dice Similarity Coefficient (DSC) and accuracy. Subsequently, the research gaps and risks, related to distinct segmentation schemes are considered for directing the researchers towards a better future investigation field.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
497--521
Opis fizyczny
Bibliogr. 50 poz., rys.
Twórcy
autor
- Noorul Islam University, Kumaracoil – 629 180, India
autor
- Department of Information Technology, Noorul Islam University, Kumaracoil – 629 180, India
Bibliografia
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- [34] Mukherjee, D.P., Cheng, I., Ray, N., Mushahwar, V., Lebel, M. and Basu, A. (2010) Automatic segmentation of spinal cord MRI using symmetric boundary tracing. IEEE Transactions on Information Technology in Biomedicine, 14, 5, 1275-1278.
- [35] Nasiri, F. and Zade H. S. (2013) Automatic segmentation of interverte-bral disk from MR images of the spine based on graph cut method. In: Proceedings of 2013 8th IEEE Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE, 300-303.
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- [39] Perone, C.S., Calabrese, E. and Cohen-Adad, J. (2017) Spinal cord gray matter segmentation using deep dilated convolutions. Scientific Reports, 8, 1-13.
- [40] Prados, F., Cardoso, M.J., Yiannakas, M.C., Hoy, L.R., Tebaldi, E., Kearney, H., Liechti, M.D. et al. (2016) Fully automated grey and white matter spinal cord segmentation. Scientific Reports, 6, 1.
- [41] Priya, R. and Umaibanu, M. (2017) Automatic Spinal Cord Segmentation From Medical MR Images using Hybrid Algorithms. International Journal on Future Revolution in Computer Science & Communication Engineering, 3, 12, 226 – 230.
- [42] Raja’S, A., Corso, J.J., Chaudhary, V. and Dhillon, G. (2010) Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI. International Journal of Computer Assisted Radiology and Surgery, 5, 3, 287-293.
- [43] Riesenburger, R.I., Safain, M.G., Ogbuji, R., Hayes, J. and Hwang, S.W. (2015) A novel classification system of lumbar disc degeneration. Journal of Clinical Neuroscience, 22, 2, 346-351.
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- [45] Sahane, R.S. and Shinde, J.V. (2016) A Survey on Segmentation of Spine MR Images Using Superpixels. International Journal for Research in Engineering Application & Management (IJREAM), 02, 09.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1a0481fa-7884-4f14-87d5-16684cc05c50