Segmentation of Clustered Cells in Microscopy Images by Geometric PDEs and Level Sets
With the huge amount of cell images produced in bio-imaging, automatic methods for segmentation are needed in order to evaluate the content of the images with respect to types of cells and their sizes. Traditional PDE-based methods using level-sets can perform automatic segmentation, but do not perform well on images with clustered cells containing sub-structures. We present two modifications for popular methods and show the improved results.
KeywordsAutomatic Segmentation Differential Interference Contrast Active Contour Model Cell Segmentation Differential Interference Contrast Image
The work was partially supported by the mYeasty pilot-project by the Austrian GEN_AU research program (www.gen-au.at). It was carried out when A. Kuijper, Y. Zhou, and L. He were with the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Linz, Austria.
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