Intervertebral disc degeneration is an age-associated condition related to chronic back pain, while its consequences are responsible for over 90 % of spine surgical procedures. In clinical practice, MRI is the modality of reference for diagnosing disc degeneration. In this study, we worked toward 2-D semiautomatic segmentation of both normal and degenerated lumbar intervertebral discs from T2-weighted midsagittal MR images of the spine. This task is challenged by partial volume effects and overlapping gray-level values between neighboring tissue classes. To overcome these problems three variations of atlas-based segmentation using a probabilistic atlas of the intervertebral disc were developed and their accuracies were quantitatively evaluated against manually segmented data. The best overall performance, when considering the tradeoff between segmentation accuracy and time efficiency, was accomplished by the atlas-robust-fuzzy c-means approach, which combines prior anatomical knowledge by means of a rigidly registered probabilistic disc atlas with fuzzy clustering techniques incorporating smoothness constraints. The dice similarity indexes of this method were 91.6 % for normal and 87.2 % for degenerated discs. Research in progress utilizes the proposed approach as part of a computer-aided diagnosis system for quantification and characterization of disc degeneration severity. Moreover, this approach could be exploited in computer-assisted spine surgery.
A new method of optimized efficiency for the retrospective reconstruction of tomograms is presented. The method has been developed for use with isocentric fluoroscopic units and is capable of performing digital tomosynthesis of anatomical planes of user selected orientation and distance from the isocenter. Optimization of efficiency has been achieved by segmenting the reconstruction process into discrete transformations that are specific to groups of pixels, rather than performing pixel by pixel operations. These involve a number of projections of the acquired image matrices as well as parallel translations and summing. Application of this method has resulted in a significant reduction of computing time. The proposed algorithm has been experimentally tested on a radiotherapy simulator unit with the use of a phantom and the obtained results are reported and discussed.
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