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Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter

Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter

Ibrahim Guelzim, Amina Amkoui, Hammadi Nait-Charif
Copyright: © 2020 |Volume: 10 |Issue: 2 |Pages: 16
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781799807360|DOI: 10.4018/IJCVIP.2020040103
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MLA

Guelzim, Ibrahim, et al. "Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter." IJCVIP vol.10, no.2 2020: pp.29-44. http://doi.org/10.4018/IJCVIP.2020040103

APA

Guelzim, I., Amkoui, A., & Nait-Charif, H. (2020). Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter. International Journal of Computer Vision and Image Processing (IJCVIP), 10(2), 29-44. http://doi.org/10.4018/IJCVIP.2020040103

Chicago

Guelzim, Ibrahim, Amina Amkoui, and Hammadi Nait-Charif. "Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter," International Journal of Computer Vision and Image Processing (IJCVIP) 10, no.2: 29-44. http://doi.org/10.4018/IJCVIP.2020040103

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Abstract

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.‎

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