Abstract
The center line algorithms in atherosclerosis follow the random directions that arteries and vessels take and are devoted to providing information that scientists use to establish parallel planes where quantifications could be accomplished with accuracy. Unfortunately, during the center line construction, high-intensity regions representing pathologies such as atheromas and calcifications mislead algorithms and thus medical verdicts. Physicians and developers assume that Computer Tomography intensities measured in Hounsfield units are stable and reproducible among scanners. However, experiments presented in this document suggest significant variabilities in the magnitudes obtained by four different institutions. The current work provides a methodology to dynamically estimate the thresholds to separate the various structures within arteries and present enough evidence of segmentation separability in the four studied institutions. Furthermore, the proposed methods are easily translated to other centers.
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Yepes-Calderon, F. (2022). Statistical Characterization of Image Intensities in Regions Inside Arteries to Facilitate the Extraction of Center Lines in Atherosclerosis Frameworks. In: Florez, H., Gomez, H. (eds) Applied Informatics. ICAI 2022. Communications in Computer and Information Science, vol 1643. Springer, Cham. https://doi.org/10.1007/978-3-031-19647-8_11
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