Abstract
In this work we propose a technique to automatically estimate circular cross-sections of the vessels in CT scans. First, a circular contour is extracted for each slice of the CT by using the Hough transform. Afterward, the locations of the circles are optimized by means of a parametric snake model, and those circles which best fit the contours of the vessels are selected by applying a robust quality criterion. Finally, this collection of circles is used to estimate the local probability density functions of the image intensity inside and outside the vessels. We present a large variety of experiments on CT scans which show the reliability of the proposed method.
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Acknowledgement
This research has partially been supported by the MINECO projects references TIN2016-76373-P (AEI/FEDER, UE) and MTM2016-75339-P (AEI/FEDER, UE) (Ministerio de Economía y Competitividad, Spain). The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.
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Alvarez, L. et al. (2017). Robust Detection of Circles in the Vessel Contours and Application to Local Probability Density Estimation. In: Cardoso, M., et al. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS STENT CVII 2017 2017 2017. Lecture Notes in Computer Science(), vol 10552. Springer, Cham. https://doi.org/10.1007/978-3-319-67534-3_1
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DOI: https://doi.org/10.1007/978-3-319-67534-3_1
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