Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology

A. Dufour, O. Tankyevych, B. Naegel, H. Talbot, C. Ronse, J. Baruthio, P. Dokládal, N. Passat
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kpkfymbkufcnzjfc5ydyokby4y" style="color: black;">Medical Image Analysis</a> </i> &nbsp;
In the last 20 years, 3D angiographic imaging proved its usefulness in the context of various clinical applications. However, angiographic images are generally difficult to analyse due to their size and the fact that useful information is easily hidden in noise and artifacts. Therefore, there is an ongoing necessity to provide tools facilitating their visualization and analysis, while vessel segmentation from such images remains a challenging task. This article presents new vessel segmentation
more &raquo; ... nd filtering techniques, relying on recent advances in mathematical morphology. In particular, methodological results related to variant mathematical morphology and connected filtering are stated, and involved in an angiographic data processing framework. These filtering and segmentation methods are validated on real and synthetic 3D angiographic data.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.media.2012.08.004">doi:10.1016/j.media.2012.08.004</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/23168165">pmid:23168165</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c4q3vizqznerxpqeju7s3dwd5y">fatcat:c4q3vizqznerxpqeju7s3dwd5y</a> </span>
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