Kuijper, ArjanArjanKuijperHeise, B.B.HeiseZhou, Y.Y.ZhouHe, L.L.HeWolinski, H.H.WolinskiKohlwein, S.S.Kohlwein2022-03-052022-03-052015https://publica.fraunhofer.de/handle/publica/24557110.1007/978-0-387-09749-7_262-s2.0-84944252145With 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.enBusiness Field: Virtual engineeringResearch Line: Computer vision (CV)image analysispartial differential equationimage classificationimage processinglevel set006book article