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dc.contributor.authorHodneland, Erlend
dc.contributor.authorTai, Xue-Cheng
dc.contributor.authorKalisch, Henrik
dc.PublishedIEEE Transactions on Medical Imaging 2016, 35(4):957-966eng
dc.description.abstractWatershed segmentation is useful for a number of image segmentation problems with a wide range of practical applications. Traditionally, the tracking of the immersion front is done by applying a fast sorting algorithm. In this work, we explore a continuous approach based on a geometric description of the immersion front which gives rise to a partial differential equation. The main advantage of using a partial differential equation to track the immersion front is that the method becomes versatile and may easily be stabilized by introducing regularization terms. Coupling the geometric approach with a proper “merging strategy” creates a robust algorithm which minimizes over- and under-segmentation even without predefined markers. Since reliable markers defined prior to segmentation can be difficult to construct automatically for various reasons, being able to treat marker-free situations is a major advantage of the proposed method over earlier watershed formulations. The motivation for the methods developed in this paper is taken from high-throughput screening of cells. A fully automated segmentation of single cells enables the extraction of cell properties from large data sets, which can provide substantial insight into a biological model system. Applying smoothing to the boundaries can improve the accuracy in many image analysis tasks requiring a precise delineation of the plasma membrane of the cell. The proposed segmentation method is applied to real images containing fluorescently labeled cells, and the experimental results show that our implementation is robust and reliable for a variety of challenging segmentation tasks.en_US
dc.rightsAttribution CC BYeng
dc.subjectBiomedical image processingeng
dc.subjectpartial differential equationseng
dc.subjectlevel seteng
dc.subjectobject segmentationeng
dc.subjectobject recognitioneng
dc.subjectcells (biology)eng
dc.titlePDE Based Algorithms for Smooth Watershedsen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright 2016 The Author(s)en_US

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