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 |Pages: 16
DOI: 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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The tracking of an object in an images sequence consists of estimating the position of this object in each frame of the sequence. The tracked object can be rigid or non-rigid, simple or articulated. During the tracking, several difficulties are encountered, some related to the tracked object: rotation, sudden/unpredictable movement, or partial/total occlusion. Other difficulties are related to the conditions of the scene: change in the scene illumination‎,‎ ‏variable background or degradation of the images quality due to the nature of the sequences or to ‎an introduced noise‎.

Object tracking is realised in two phases (for each frame of the sequence): the first step is to search similar objects to the target, and then, choose the most likely one ‎according to a similarity measure as, the cross-correlation (Bifulco, Cesarelli, Allen, Sansone, & Bracale, 2001), the sum of squared differences (Hager, Dewan, & Stewart, 2004), the Bhattacharyya coefficient (Shirinzadeh, Seyedarabi, & Aghagolzadeh, 2012), mutual information (Panin & Knoll, 2008) or correlation coefficient (Qin & Pun, 2018).

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