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Segmentation of clustered nuclei based on concave curve expansion
Author(s) -
ZHANG C.,
SUN C.,
PHAM T.D.
Publication year - 2013
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12043
Subject(s) - segmentation , artificial intelligence , watershed , pattern recognition (psychology) , computer science , image segmentation , computer vision , scale space segmentation , image (mathematics) , segmentation based object categorization
Summary 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.