People: Raja Alomari, Jason Corso, Vipin Chaudhary, and Gurmeet Dhillon

According to the National Institute of Neurological Disorders and Stroke (NINDS), back pain is the second most common neurological ailment in the United States after headache. Over 12 million Americans have some sort of Intervertebral Disc Disease (IDD). Localization and labeling of the vertebral column anatomical structures has thus been a focus of recent studies, due to the high demand of analysis of the vertebral column structures such as disc size, disc shape, and water content percentage in discs. This analysis is a core requirement for the diagnosis of the vertebral column as a whole and for anatomical structures such as discs, vertebrae and soft tissues.

Accurate labeling of the backbone structures is a necessary step for performing any type of analysis, diagnosis, or surgical planning. One key use of labeling is the design of a computer aided diagnosis system for lumbar area. In the clinical practice, the neuroradiologist reports the diagnosis at each disc level. However, although accurate labeling of the backbone structures is necessary, the backbone has wide variabilities including degree of bending of the vertebral column, sizes, shapes, count (number) and appearances of discs and vertebrae. In addition, existing abnormality conditions such as vertebral fusion, degenerative disc diseases, spinal infections, and spinal scoliosis add more variability. We have developed a two-level probabilistic model for such disc localization and labeling (illustration below). Whereas conventional labeling approaches (e.g., our approach to brain tumor above) define all models at the pixel level, our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location and shape. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We have presented accurate results, about 89% accuracy on 105 normal and abnormal cases (96% when using normal alone and 87% when using abnormal alone). Various diseases that affect the vertebral column are usually painful and influence the patientŐs everyday life. We are concerned with the following abnormalities: disc herniation, spinal stenosis, disc degeneration, disc desiccation, and spinal infection. Automated detection and diagnosis of these abnormalities holds great promise is in clinical practice, especially with the increasing incidents of back problems outpacing the numbers of radiologists. We have investigated numerous approaches to diagnose the presence of an abnormality, the class of the abnormality, and quantification of the abnormality's parameters. Our results are described in full detail in the publications listed below.

• Raja's website lists more information

[1]	R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon. Lumbar spine disc herniation diagnosis with a joint shape model. In Proceedings of Medical Image Computing and Computer Aided Intervention Workshop on Computational Spine Imaging, 2013. [ bib | .pdf ]
[2]	R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon. Toward a clinical lumbar CAD: Herniation diagnosis. International Journal of Computer Aided Radiology and Surgery, 6(1):119-126, 2011. [ bib | .pdf ]
[3]	R. S. Alomari, J. J. Corso, and V. Chaudhary. Labeling of lumbar discs using both pixel- and object-level features with a two-level probabilistic model. IEEE Transactions on Medical Imaging, 30(1):1-10, 2011. [ bib | .pdf ]
[4]	R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon. Lumbar disc herniation cad with a GVF-snake model. In Proceedings of the 24th International Conference on Computer Aided Diagnosis and Surgery (CARS '10), 2010. [ bib | .pdf ]
[5]	R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon. Automatic diagnosis of lumbar disc herniation using shape and appearance features from mri. In Proceedings of SPIE Conference on Medical Imaging, 2010. [ bib | .pdf ]
[6]	R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon. Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI. International Journal of Computer Aided Radiology and Surgery, 5(3):287-293, 2010. [ bib | .pdf ]
[7]	R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon. Desiccation diagnosis in lumbar discs from clinical mri with a probabilistic model. In Proceedings of 2009 IEEE International Symposium on Biomedical Imaging, 2009. [ bib | .pdf ]
[8]	R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon. 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), 2009. [ bib | .pdf ]
[9]	J. J. Corso, R. S. Alomari, and V. Chaudhary. Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In Proceedings of Medical Image Computing and Computer Aided Intervention (MICCAI), volume LNCS 5241 Part 1, pages 202-210, 2008. [ bib | .pdf ]