Vertebrae tracking in videofluoroscopy is a challenging problem because of the low quality of image sequences, like poor image contrast, ambiguous geometry details, and vertebrae rotation. The aim of this article is to tackle this problem by proposing a method for rigid object tracking based on the fragmentation of the tracked object. The proposed method is based on the particle filter using the calculation of the similarity between the respective fragments of objects instead of the whole objects. The similarity measures used are the Jaccard index, the correlation coefficient, and the Bhattacharyya coefficient. The tracking starts with a semi-automatic initialization. The results show that the fragments-based object tracking method outperforms the classical method (without fragmentation) for each of the used similarity measures. The results show that the tracking based on the Jaccard index is more stable and outperforms methods based on other similarity measures.
Tracking of spinal vertebrae in videofluoroscopy video is useful for diagnosis of certain spine pathologies, such as vertebral fractures, spondylolisthesis and low back pain. The aim of this work is to improve a semi-automatic method for vertebrae tracking based on particle filter. The process starts with vertebrae model construction by splining hand-selected landmark points, then the particle filter tracks the vertebrae in each frame. While the process successfully tracks the vertebras, the trajectories are quite jittery This paper aims to improve the overall tracking by smoothing the tracking trajectory using curve fitting function. The tracking results are more natural with very little noise.
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