Abstract
Accurate Vertebra localization presents an essential step for automating the diagnosis of many spinal disorders. In case of MR images of lumbar spine, this task becomes more challenging due to vertebra complex shape and high variation of soft tissue. In this paper, we propose an efficient framework for spine curve extraction and vertebra localization in T1-weighted MR images. Our method is a fast parametrized algorithm based on three steps: 1. Image enhancing 2. Meanshift clustering [5] 3. Pattern recognition techniques. We propose also an adapted and effective exploitation of new parallel and hybrid platforms, that consist of both central (CPU) and graphic (GPU) processing units, in order to accelerate our vertebra localization method. The latter can exploit both NVIDIA and ATI graphic cards since we propose CUDA and OpenCL implementations of our vertebra localization steps. Our experiments are conducted using 16 MR images of lumbar spine. The related results achieved a vertebra detection rate of 95% with an acceleration ranging from 4 to 173 \(\times \) thanks to the exploitation of Multi-CPU/Multi-GPU platforms.
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Larhmam, M.A., Mahmoudi, S.A., Benjelloun, M., Mahmoudi, S., Manneback, P. (2014). A Portable Multi-CPU/Multi-GPU Based Vertebra Localization in Sagittal MR Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_24
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DOI: https://doi.org/10.1007/978-3-319-11755-3_24
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