An Efficient Pixel Based Cervical Disc Localization
When neck pain is associated with pain, numbness, or
weakness in the arm, shoulder, or hand, further investigation is
needed as these are symptoms indicating pressure on one or more
nerve roots. Evaluation necessitates a neurologic examination and
imaging using an MRI/CT scan. A degenerating disc loses some
thickness and is less flexible, causing inter-vertebrae space to narrow.
A radiologist diagnoses an Intervertebral Disc Degeneration (IDD) by
localizing every inter-vertebral disc and identifying the pathology in
a disc based on its geometry and appearance. Accurate localizing is
necessary to diagnose IDD pathology. But, the underlying image
signal is ambiguous: a disc’s intensity overlaps the spinal nerve
fibres. Even the structure changes from case to case, with possible
spinal column bending (scoliosis). The inter-vertebral disc
pathology’s quantitative assessment needs accurate localization of the
cervical region discs. In this work, the efficacy of multilevel set
segmentation model, to segment cervical discs is investigated. The
segmented images are annotated using a simple distance matrix.
[1] Uduma, F. U. (2011). Uncommon Types of Disc Hernia (A Report of
Three Cases and Review of Literature). Global Journal of Medical
Research, 11(2).
[2] Ohnishi, K., Miyamoto, K., Kanamori, Y., Kodama, H., Hosoe, H., &
Shimizu, K. (2005). Anterior decompression and fusion for multiple
thoracic disc herniation. Journal of Bone & Joint Surgery, British
Volume, 87(3), 356-360.
[3] Boden, S. D. (1996). Current Concepts Review-The Use of
Radiographic Imaging Studies in the Evaluation of Patients Who Have
Degenerative Disorders of the Lumbar Spine*. The Journal of Bone &
Joint Surgery, 78(1), 114-24.
[4] Key, J. A. (1945). The conservative and operative treatment of lesions of
the intervertebral discs in the low back. Surgery, 17(2), 291-303.
[5] Teplick, J. G., & Haskin, M. E. (1985). Spontaneous regression of
herniated nucleus pulposus. American journal of neuroradiology, 6(3),
331-335.
[6] Vinas, F. C., Wilner, H., & Rengachary, S. (2001). The spontaneous
resorption of herniated cervical discs. Journal of clinical Neuroscience,
8(6), 542-546.
[7] Shi, R., Sun, D., Qiu, Z., & Weiss, K. L. (2007, May). An efficient
method for segmentation of MRI spine images. In Complex Medical
Engineering, 2007. CME 2007. IEEE/ICME International Conference on
(pp. 713-717). IEEE.
[8] Peng, Z., Zhong, J., Wee, W., & Lee, J. H. (2006, January). Automated
vertebra detection and segmentation from the whole spine MR images.
In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS
2005. 27th Annual International Conference of the (pp. 2527-2530).
IEEE.
[9] Jensen, G. M. (1980). Biomechanics of the lumbar intervertebral disk: A
review. Physical therapy, 60(6), 765-773.
[10] Williams, F. M. K., & Sambrook, P. N. (2011). Neck and back pain and
intervertebral disc degeneration: role of occupational factors. Best
Practice & Research Clinical Rheumatology, 25(1), 69-79.
[11] Alomari, R. S., Corso, J. J., & Chaudhary, V. (2011). Labeling of lumbar
discs using both pixel-and object-level features with a two-level
probabilistic model.Medical Imaging, IEEE Transactions on, 30(1), 1-
10.
[12] Glocker, B., Zikic, D., Konukoglu, E., Haynor, D. R., & Criminisi, A.
(2013). Vertebrae Localization in Pathological Spine CT via Dense
Classification from Sparse Annotations. In Medical Image Computing
and Computer-Assisted Intervention–MICCAI 2013 (pp. 262-270).
Springer Berlin Heidelberg.
[13] Kelm, B. M., Zhou, S. K., Suehling, M., Zheng, Y., Wels, M., &
Comaniciu, D. (2011). Detection of 3D spinal geometry using iterated
marginal space learning. In Medical Computer Vision. Recognition
Techniques and Applications in Medical Imaging (pp. 96-105). Springer
Berlin Heidelberg.
[14] Michopoulou, S., Boniatis, I., Costaridou, L., Cavouras, D.,
Panagiotopoulos, E., & Panayiotakis, G. (2009). Computer assisted
characterization of cervical intervertebral disc degeneration in MRI.
Journal of Instrumentation, 4(05), P05022.
[15] Alomari, R. S., Corso, J. J., Chaudhary, V., & Dhillon, G. (2009, June).
Desiccation diagnosis in lumbar discs from clinical mri with a
probabilistic model. In Biomedical Imaging: From Nano to Macro,
2009. ISBI'09. IEEE International Symposium on (pp. 546-549). IEEE.
[16] Ghosh, S., Malgireddy, M. R., Chaudhary, V., & Dhillon, G. (2012,
May). A new approach to automatic disc localization in clinical lumbar
mri: Combining machine learning with heuristics. In Biomedical
Imaging (ISBI), 2012 9th IEEE International Symposium on (pp. 114-
117). IEEE.
[17] Koh, J., Scott, P. D., Chaudhary, V., & Dhillon, G. (2011, March). An
automatic segmentation method of the spinal canal from clinical MR
images based on an attention model and an active contour model. In
Biomedical Imaging: From Nano to Macro, 2011 IEEE International
Symposium on (pp. 1467-1471). IEEE.
[18] Schmidt, S., Kappes, J., Bergtholdt, M., Pekar, V., Dries, S., Bystrov,
D., & Schnörr, C. (2007, January). Spine detection and labeling using a
parts-based graphical model. In Information Processing in Medical
Imaging (pp. 122-133). Springer Berlin Heidelberg.
[19] Jerebko, A. K., Schmidt, G. P., Zhou, X., Bi, J., Anand, V., Liu, J., ... &
Krishnan, A. (2007, January). Robust parametric modeling approach
based on domain knowledge for computer aided detection of vertebrae
column metastases in MRI. In Information Processing in Medical
Imaging (pp. 713-724). Springer Berlin Heidelberg.
[20] Wong, A., Mishra, A., Yates, J., Fieguth, P., Clausi, D. A., & Callaghan,
J. P. (2009). Intervertebral disc segmentation and volumetric
reconstruction from peripheral quantitative computed tomography
imaging. Biomedical Engineering, IEEE Transactions on, 56(11), 2748-
2751.
[21] Glocker, B., Feulner, J., Criminisi, A., Haynor, D. R., & Konukoglu, E.
(2012). Automatic localization and identification of vertebrae in
arbitrary field-of-view CT scans. In Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2012 (pp. 590-598). Springer
Berlin Heidelberg.
[22] Oktay, A. B., & Akgul, Y. S. (2011). Localization of the Lumbar discs
using machine learning and exact probabilistic inference. In Medical
Image Computing and Computer-Assisted Intervention–MICCAI 2011
(pp. 158-165). Springer Berlin Heidelberg.
[23] Bhole, C., Kompalli, S., & Chaudhary, V. (2009, February). Context
sensitive labeling of spinal structure in MR images. In SPIE Medical
Imaging (pp. 72603P-72603P). International Society for Optics and
Photonics. [24] Pekar, V., Bystrov, D., Heese, H. S., Dries, S. P., Schmidt, S., Grewer,
R., ... & Van Muiswinkel, A. M. (2007). Automated planning of scan
geometries in spine MRI scans. In Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2007 (pp. 601-608). Springer
Berlin Heidelberg.
[25] Huang, S. H., Lai, S. H., & Novak, C. L. (2008, May). A statistical
learning appproach to vertebra detection and segmentation from spinal
MRI. InBiomedical Imaging: From Nano to Macro, 2008. ISBI 2008.
5th IEEE International Symposium on (pp. 125-128). IEEE.
[26] da Rocha Neto, A. R., Sousa, R., Barreto, G. D. A., & Cardoso, J. S.
(2011). Diagnostic of pathology on the vertebral column with embedded
reject option. InPattern Recognition and Image Analysis (pp. 588-595).
Springer Berlin Heidelberg.
[27] Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise
smooth functions and associated variational problems. Communications
on pure and applied mathematics, 42(5), 577-685.
[28] Morel, J. M., & Solimini, S. (1995). Variational methods in image
segmentation. Birkhauser Boston Inc..
[29] Morel, J. M., & Solimini, S. (1989). Segmentation d'images par méthode
variationnelle: une preuve constructive d'existence. Comptes rendus de
l'Académie des sciences. Série 1, Mathématique, 308(15), 465-470.
[30] Jean-Michel, M. O., OLIMINI, S. S., & de Lattre, P. D. M. (1988).
Segmentation of Images by Variational Methods: a Constructive
Approach. Rev. Mat. Complut,1(1), 2-3.
[31] Massari, U., & Tamanini, I. (1993). On the finiteness of optimal
partitions. Annali dell’Università’di Ferrara, 39(1), 167-185.
[32] Tamanini, I. (1996). Optimal approximation by piecewise constant
functions. InVariational Methods for Discontinuous Structures (pp. 73-
85). Birkhäuser Basel.
[33] Tamanini, I., & Congedo, G. (1996). Optimal segmentation of
unbounded functions. Rendiconti del Seminario Matematico della
Università di Padova, 95, 153-174.
[34] Leonardi, G. P., & Tamanini, I. (1998). On minimizing partitions with
infinitely many components. Annali dell’Università di Ferrara, 44(1),
41-57.
[1] Uduma, F. U. (2011). Uncommon Types of Disc Hernia (A Report of
Three Cases and Review of Literature). Global Journal of Medical
Research, 11(2).
[2] Ohnishi, K., Miyamoto, K., Kanamori, Y., Kodama, H., Hosoe, H., &
Shimizu, K. (2005). Anterior decompression and fusion for multiple
thoracic disc herniation. Journal of Bone & Joint Surgery, British
Volume, 87(3), 356-360.
[3] Boden, S. D. (1996). Current Concepts Review-The Use of
Radiographic Imaging Studies in the Evaluation of Patients Who Have
Degenerative Disorders of the Lumbar Spine*. The Journal of Bone &
Joint Surgery, 78(1), 114-24.
[4] Key, J. A. (1945). The conservative and operative treatment of lesions of
the intervertebral discs in the low back. Surgery, 17(2), 291-303.
[5] Teplick, J. G., & Haskin, M. E. (1985). Spontaneous regression of
herniated nucleus pulposus. American journal of neuroradiology, 6(3),
331-335.
[6] Vinas, F. C., Wilner, H., & Rengachary, S. (2001). The spontaneous
resorption of herniated cervical discs. Journal of clinical Neuroscience,
8(6), 542-546.
[7] Shi, R., Sun, D., Qiu, Z., & Weiss, K. L. (2007, May). An efficient
method for segmentation of MRI spine images. In Complex Medical
Engineering, 2007. CME 2007. IEEE/ICME International Conference on
(pp. 713-717). IEEE.
[8] Peng, Z., Zhong, J., Wee, W., & Lee, J. H. (2006, January). Automated
vertebra detection and segmentation from the whole spine MR images.
In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS
2005. 27th Annual International Conference of the (pp. 2527-2530).
IEEE.
[9] Jensen, G. M. (1980). Biomechanics of the lumbar intervertebral disk: A
review. Physical therapy, 60(6), 765-773.
[10] Williams, F. M. K., & Sambrook, P. N. (2011). Neck and back pain and
intervertebral disc degeneration: role of occupational factors. Best
Practice & Research Clinical Rheumatology, 25(1), 69-79.
[11] Alomari, R. S., Corso, J. J., & Chaudhary, V. (2011). Labeling of lumbar
discs using both pixel-and object-level features with a two-level
probabilistic model.Medical Imaging, IEEE Transactions on, 30(1), 1-
10.
[12] Glocker, B., Zikic, D., Konukoglu, E., Haynor, D. R., & Criminisi, A.
(2013). Vertebrae Localization in Pathological Spine CT via Dense
Classification from Sparse Annotations. In Medical Image Computing
and Computer-Assisted Intervention–MICCAI 2013 (pp. 262-270).
Springer Berlin Heidelberg.
[13] Kelm, B. M., Zhou, S. K., Suehling, M., Zheng, Y., Wels, M., &
Comaniciu, D. (2011). Detection of 3D spinal geometry using iterated
marginal space learning. In Medical Computer Vision. Recognition
Techniques and Applications in Medical Imaging (pp. 96-105). Springer
Berlin Heidelberg.
[14] Michopoulou, S., Boniatis, I., Costaridou, L., Cavouras, D.,
Panagiotopoulos, E., & Panayiotakis, G. (2009). Computer assisted
characterization of cervical intervertebral disc degeneration in MRI.
Journal of Instrumentation, 4(05), P05022.
[15] Alomari, R. S., Corso, J. J., Chaudhary, V., & Dhillon, G. (2009, June).
Desiccation diagnosis in lumbar discs from clinical mri with a
probabilistic model. In Biomedical Imaging: From Nano to Macro,
2009. ISBI'09. IEEE International Symposium on (pp. 546-549). IEEE.
[16] Ghosh, S., Malgireddy, M. R., Chaudhary, V., & Dhillon, G. (2012,
May). A new approach to automatic disc localization in clinical lumbar
mri: Combining machine learning with heuristics. In Biomedical
Imaging (ISBI), 2012 9th IEEE International Symposium on (pp. 114-
117). IEEE.
[17] Koh, J., Scott, P. D., Chaudhary, V., & Dhillon, G. (2011, March). An
automatic segmentation method of the spinal canal from clinical MR
images based on an attention model and an active contour model. In
Biomedical Imaging: From Nano to Macro, 2011 IEEE International
Symposium on (pp. 1467-1471). IEEE.
[18] Schmidt, S., Kappes, J., Bergtholdt, M., Pekar, V., Dries, S., Bystrov,
D., & Schnörr, C. (2007, January). Spine detection and labeling using a
parts-based graphical model. In Information Processing in Medical
Imaging (pp. 122-133). Springer Berlin Heidelberg.
[19] Jerebko, A. K., Schmidt, G. P., Zhou, X., Bi, J., Anand, V., Liu, J., ... &
Krishnan, A. (2007, January). Robust parametric modeling approach
based on domain knowledge for computer aided detection of vertebrae
column metastases in MRI. In Information Processing in Medical
Imaging (pp. 713-724). Springer Berlin Heidelberg.
[20] Wong, A., Mishra, A., Yates, J., Fieguth, P., Clausi, D. A., & Callaghan,
J. P. (2009). Intervertebral disc segmentation and volumetric
reconstruction from peripheral quantitative computed tomography
imaging. Biomedical Engineering, IEEE Transactions on, 56(11), 2748-
2751.
[21] Glocker, B., Feulner, J., Criminisi, A., Haynor, D. R., & Konukoglu, E.
(2012). Automatic localization and identification of vertebrae in
arbitrary field-of-view CT scans. In Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2012 (pp. 590-598). Springer
Berlin Heidelberg.
[22] Oktay, A. B., & Akgul, Y. S. (2011). Localization of the Lumbar discs
using machine learning and exact probabilistic inference. In Medical
Image Computing and Computer-Assisted Intervention–MICCAI 2011
(pp. 158-165). Springer Berlin Heidelberg.
[23] Bhole, C., Kompalli, S., & Chaudhary, V. (2009, February). Context
sensitive labeling of spinal structure in MR images. In SPIE Medical
Imaging (pp. 72603P-72603P). International Society for Optics and
Photonics. [24] Pekar, V., Bystrov, D., Heese, H. S., Dries, S. P., Schmidt, S., Grewer,
R., ... & Van Muiswinkel, A. M. (2007). Automated planning of scan
geometries in spine MRI scans. In Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2007 (pp. 601-608). Springer
Berlin Heidelberg.
[25] Huang, S. H., Lai, S. H., & Novak, C. L. (2008, May). A statistical
learning appproach to vertebra detection and segmentation from spinal
MRI. InBiomedical Imaging: From Nano to Macro, 2008. ISBI 2008.
5th IEEE International Symposium on (pp. 125-128). IEEE.
[26] da Rocha Neto, A. R., Sousa, R., Barreto, G. D. A., & Cardoso, J. S.
(2011). Diagnostic of pathology on the vertebral column with embedded
reject option. InPattern Recognition and Image Analysis (pp. 588-595).
Springer Berlin Heidelberg.
[27] Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise
smooth functions and associated variational problems. Communications
on pure and applied mathematics, 42(5), 577-685.
[28] Morel, J. M., & Solimini, S. (1995). Variational methods in image
segmentation. Birkhauser Boston Inc..
[29] Morel, J. M., & Solimini, S. (1989). Segmentation d'images par méthode
variationnelle: une preuve constructive d'existence. Comptes rendus de
l'Académie des sciences. Série 1, Mathématique, 308(15), 465-470.
[30] Jean-Michel, M. O., OLIMINI, S. S., & de Lattre, P. D. M. (1988).
Segmentation of Images by Variational Methods: a Constructive
Approach. Rev. Mat. Complut,1(1), 2-3.
[31] Massari, U., & Tamanini, I. (1993). On the finiteness of optimal
partitions. Annali dell’Università’di Ferrara, 39(1), 167-185.
[32] Tamanini, I. (1996). Optimal approximation by piecewise constant
functions. InVariational Methods for Discontinuous Structures (pp. 73-
85). Birkhäuser Basel.
[33] Tamanini, I., & Congedo, G. (1996). Optimal segmentation of
unbounded functions. Rendiconti del Seminario Matematico della
Università di Padova, 95, 153-174.
[34] Leonardi, G. P., & Tamanini, I. (1998). On minimizing partitions with
infinitely many components. Annali dell’Università di Ferrara, 44(1),
41-57.
@article{"International Journal of Information, Control and Computer Sciences:70206", author = "J. Preetha and S. Selvarajan", title = "An Efficient Pixel Based Cervical Disc Localization", abstract = "When neck pain is associated with pain, numbness, or
weakness in the arm, shoulder, or hand, further investigation is
needed as these are symptoms indicating pressure on one or more
nerve roots. Evaluation necessitates a neurologic examination and
imaging using an MRI/CT scan. A degenerating disc loses some
thickness and is less flexible, causing inter-vertebrae space to narrow.
A radiologist diagnoses an Intervertebral Disc Degeneration (IDD) by
localizing every inter-vertebral disc and identifying the pathology in
a disc based on its geometry and appearance. Accurate localizing is
necessary to diagnose IDD pathology. But, the underlying image
signal is ambiguous: a disc’s intensity overlaps the spinal nerve
fibres. Even the structure changes from case to case, with possible
spinal column bending (scoliosis). The inter-vertebral disc
pathology’s quantitative assessment needs accurate localization of the
cervical region discs. In this work, the efficacy of multilevel set
segmentation model, to segment cervical discs is investigated. The
segmented images are annotated using a simple distance matrix.", keywords = "Intervertebral Disc Degeneration (IDD), Cervical
Disc Localization, multilevel set segmentation.", volume = "9", number = "2", pages = "597-6", }
