ArticleJournal of biomedical optics2013
Extraction of target specimens from bioholographic images using interactive graph cuts.
Article in Journal of biomedical optics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Automated red blood cells extraction from holographic images using fully convolutional neural networks.Biomedical optics express · 2017Article
- Automatic extraction of cell nuclei from H&E-stained histopathological images.Journal of medical imaging (Bellingham, Wash.) · 2017Article
- Cell morphology-based classification of red blood cells using holographic imaging informatics.Biomedical optics express · 2016Article
- Image analysis of anatomical traits in stalk transections of maize and other grasses.Plant methods · 2015Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is necessary to extract target specimens from bioholographic images for high-level analysis such as object identification, recognition, and tracking with the advent of application of digital holographic microscopy to transparent or semi-transparent biological specimens. We present an interactive graph cuts approach to segment the needed target specimens in the reconstructed bioholographic images. This method combines both regional and boundary information and is robust to extract targets with weak boundaries. Moreover, this technique can achieve globally optimal results while minimizing an energy function. We provide a convenient user interface, which can easily differentiate the foreground/background for various types of holographic images, as well as a dynamically modified coefficient, which specifies the importance of the regional and boundary information. The extracted results from our scheme have been compared with those from an advanced level-set-based segmentation method using an unbiased comparison algorithm. Experimental results show that this interactive graph cut technique can not only extract different kinds of target specimens in bioholographic images, but also yield good results when there are multiple similar objects in the holographic image or when the object boundaries are very weak.
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