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A Review of Watershed Implementations for Segmentation of Volumetric Images

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dc.contributor.authorYakimchuk, I.
dc.contributor.authorKornilov, A.
dc.contributor.authorSafonov, I.
dc.contributor.authorКорнилов, Антон Сергеевич
dc.contributor.authorСафонов, Илья Владимирович
dc.date.accessioned2024-12-25T13:41:09Z
dc.date.available2024-12-25T13:41:09Z
dc.date.issued2022
dc.description.abstract© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm–watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed.
dc.identifier.citationYakimchuk, I. A Review of Watershed Implementations for Segmentation of Volumetric Images / Yakimchuk, I., Kornilov, A., Safonov, I. // Journal of Imaging. - 2022. - 8. - № 5. - 10.3390/jimaging8050127
dc.identifier.doi10.3390/jimaging8050127
dc.identifier.urihttps://www.doi.org/10.3390/jimaging8050127
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85129907906&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000803347500001
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/27886
dc.relation.ispartofJournal of Imaging
dc.titleA Review of Watershed Implementations for Segmentation of Volumetric Images
dc.typeReview
dspace.entity.typePublication
oaire.citation.issue5
oaire.citation.volume8
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