Clustered nuclei splitting using curvature information
2011 (English)Conference paper (Refereed)Text
Automated splitting of clustered nuclei from images of tissue sections is essential to many biomedical studies. Many existing image segmentation methods tend to produce over-segmented or under-segmented results for clustered nuclei images. In this paper, a new curvature information based image segmentation algorithm is proposed. Through combining curvature information with a distance map, our algorithm can extract correct markers corresponding to each nucleus. Afterwards, marker based watershed segmentation is used to segment the clustered nuclei. The algorithm is tested on both synthetic and real images. Experimental results show that our algorithm is accurate and robust to noise in segmentation of clustered nuclei.
Place, publisher, year, edition, pages
2011. 352-357 p.
Image segmentation; Curvature information; Clustered cell nuclei; Watershed segmentation
Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-127906DOI: 10.1109/DICTA.2011.66ISBN: 978-1-4577-2006-2OAI: oai:DiVA.org:liu-127906DiVA: diva2:928793
2011 International Conference on Digital Image Computing: Techniques and Applications. 6-8 December 2011, Noosa, QLD