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Application of centerline detection and deformable contours algorithms to segmenting the carotid lumen

[+] Author Affiliations
Tomasz Hachaj

Pedagogical University of Krakow, Institute of Computer Science and Computer Methods, 2 Podchorazych Avenue, 30-084 Krakow, Poland

Marek R. Ogiela

AGH University of Science and Technology, 30 Mickiewicza Avenue, 30-059 Krakow, Poland

J. Electron. Imaging. 23(2), 023006 (Mar 17, 2014). doi:10.1117/1.JEI.23.2.023006
History: Received April 11, 2013; Revised January 13, 2014; Accepted February 14, 2014
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Abstract.  The main contribution of this article is to evaluate the utility of different state-of-the-art deformable contour models for segmenting carotid lumen walls from computed tomography angiography images. We have also proposed and tested a new tracking-based lumen segmentation method based on our evaluation results. The deformable contour algorithm (snake) is used to detect the outer wall of the vessel. We have examined four different snakes: with a balloon, distance, and a gradient vector flow force and the method of active contours without edges. The algorithms were evaluated on a set of 32 artery lumens—16 from the common carotid artery (CCA)-the internal carotid artery section and 16 from the CCA-the external carotid artery section—in order to find the optimum deformable contour model for this task. Later, we evaluated different values of energy terms in the method of active contours without edges, which turned out to be the best for our dataset, in order to find the optimal values for this particular segmentation task. The choice of particular weights in the energy term was evaluated statistically. The final Dice’s coefficient at the level of 0.939±0.049 puts our algorithm among the best state-of-the-art methods for these solutions.

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Citation

Tomasz Hachaj and Marek R. Ogiela
"Application of centerline detection and deformable contours algorithms to segmenting the carotid lumen", J. Electron. Imaging. 23(2), 023006 (Mar 17, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.2.023006


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