@article {272,
	title = {Intervertebral disc segmentation and volumetric reconstruction from peripheral quantitative computed tomography imaging},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {56},
	year = {2010},
	pages = {2748 - 2751},
	abstract = {<p>An automatic system for segmenting and constructing volumetric representations of excised intervertebral discs from peripheral quantitative computed tomography (PQCT) imagery is presented. The system is designed to allow for automatic quantitative analysis of progressive herniation damage to the intervertebral discs under flexion/extension motions combined with a compressive load. Automatic segmentation and volumetric reconstruction of intervertebral disc from PQCT imagery is a very challenging problem due to factors such as streak artifacts and unclear material density separation between contrasted intervertebral disc and surrounding bone in the PQCT imagery, as well as the formation of multiple contrasted regions under axial scans. To address these factors, a novel multiscale level set approach based on the Mumford-Shaft energy functional in iterative bilateral scale space is employed to segment the intervertebral disc regions from the PQCT imagery. A Delaunay triangulation is then performed based on the set of points associated with the intervertebral disc regions to construct the volumetric representation of the intervertebral disc. Experimental results show that the proposed system achieves segmentation and volumetric reconstructions of intervertebral discs with mean absolute distance error below 0.8 mm when compared to ground truth measurements. The proposed system is currently in operational use as a visualization tool for studying progressive intervertebral disc damage.</p>
},
	keywords = {Animals, Artificial Intelligence, Automated, automatic quantitative analysis, Computer-Assisted, computerised tomography, Delaunay triangulation, flexion-extension motions, image reconstruction, image segmentation, Imaging, intervertebral disc segmentation, Intervertebral Disk, Intervertebral Disk Displacement, iterative bilateral scale space, material density separation, medical image processing, Mumford-Shah energy functional, Pattern Recognition, peripheral quantitative computed tomography imaging, progressive herniation damage, Radiographic Image Enhancement, Radiographic Image Interpretation, Swine, Three-Dimensional, Tomography, volumetric reconstruction, X-Ray Computed},
	issn = {0018-9294},
	doi = {http://dx.doi.org/10.1109/TBME.2009.2027225},
	author = {A Wong and A Mishra and J Yates and P Fieguth and D A. Clausi and J Callaghan}
}
@inproceedings {331,
	title = {Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets},
	booktitle = {30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2008},
	month = {08/2008},
	address = {Vancouver, British Columbia, Canada},
	abstract = {<p>This paper presents a novel shape-guided active contour based approach for segmenting and tracking lumbar vertebrae in video fluoroscopy using complex-valued wavelets. representations. Due to low radiation exposure levels, fluoroscopic images are characterized by low signal-to-noise ratios, low contrast resolution, and illumination non-homogeneities both spatially and temporally, making current methods ill-suited for segmenting and tracking lumbar vertebrae based on existing energy functionals. Furthermore, current methods perform poorly in situations characterized by high curvature as found in the structure of lumbar spine vertebrae. In this paper, a novel iterative estimation approach is used to determine an external energy functional based on complex wavelets. A shaped-guided algorithm is used to evolve the contour around a lumbar spine vertebra based on the complex wavelet energy. The high curvature exhibited by the lumbar spine vertebra is addressed through a novel importance sampling scheme. Experimental results show that the proposed algorithm achieves significantly better segmentation and tracking performance for lumbar spine vertebrae in fluoroscopic images when compared to existing techniques.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Female, Fluoroscopy, Humans, Lumbar Vertebrae, Male, Pattern Recognition, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity and Specificity, Video Recording, Young Adult},
	doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649290},
	author = {A Wong and A Mishra and P Fieguth and D A. Clausi and N M. Dunk and J Callaghan}
}
