title: Segmentation of clustered nuclei based on concave curve expansion.
abstract: Segmentation of nuclei from images of tissue sections is important for many biological and biomedical studies. Many existing image segmentation algorithms may lead to oversegmentation or undersegmentation for clustered nuclei images. In this paper, we proposed a new image segmentation algorithm based on concave curve expansion to correctly and accurately extract markers from the original images. Marker-controlled watershed is then used to segment the clustered nuclei. The algorithm was tested on both synthetic and real images and better results are achieved compared with some other state-of-the-art methods.
authors.affiliation: CSIRO Mathematics, Informatics and Statistics Division, Locked Bag 17, North Ryde, New South Wales 2113, Australia. kone.zhang@gmail.com
authors.forename: C
authors.initials: C
authors.lastname: Zhang
authors.forename: C
authors.initials: C
authors.lastname: Sun
authors.forename: T D
authors.initials: TD
authors.lastname: Pham
mesh_terms: D057205:Automation, Laboratory / Q000379:methods; D002467:Cell Nucleus / Q000648:ultrastructure*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D061848:Optical Imaging / Q000379:methods*