A New Model for the Segmentation of Multiple, Overlapping, Near-Circular Objects (bibtex)
by Csaba Molnar, Zoltan Kato, Ian Jermyn
Abstract:
Some of the most difficult image segmentation problems involve an unknown number of object instances that can touch or overlap in the image, e.g. microscopy imaging of cells in biology. In an important set of cases, the nature of the objects and the imaging process mean that when objects overlap, the resulting image is approximately given by the sum of intensities of individual objects; and, in addition, the objects of interest are `blob-like' or near-circular. We propose a new model for the segmentation of the objects in such images. The posterior energy is the sum of a prior energy modelling shape and a likelihood energy modelling the image. The prior is a multi-layer nonlocal phase field energy that favours configurations consisting of a number of possibly overlapping or touching near-circular object instances. The likelihood energy models the additive nature of image intensity in regions corresponding to overlapping objects. We use variational methods to compute a MAP estimate of the object instances in an image. We test the resulting model on synthetic data and on fluorescence microscopy images of cell nuclei.
Reference:
Csaba Molnar, Zoltan Kato, Ian Jermyn, A New Model for the Segmentation of Multiple, Overlapping, Near-Circular Objects, In Proceedings of International Conference on Digital Image Computing: Techniques and Applications, Adelaide, Australia, pp. 1-5, 2015, IEEE.
Bibtex Entry:
@string{dicta="Proceedings of International Conference on Digital Image Computing: Techniques and Applications"}
@InProceedings{Molnar-etal2015,
  author =	 {Molnar, Csaba and Kato, Zoltan and Ian Jermyn},
  title =	 {A New Model for the Segmentation of Multiple,
                  Overlapping, Near-Circular Objects},
  booktitle =	 dicta,
  pages =	 {1--5},
  year =	 2015,
  address =	 {Adelaide, Australia},
  month =	 nov,
  publisher =	 {IEEE},
  abstract =	 {Some of the most difficult image segmentation
                  problems involve an unknown number of object
                  instances that can touch or overlap in the image,
                  e.g. microscopy imaging of cells in biology. In an
                  important set of cases, the nature of the objects
                  and the imaging process mean that when objects
                  overlap, the resulting image is approximately given
                  by the sum of intensities of individual objects;
                  and, in addition, the objects of interest are
                  `blob-like' or near-circular. We propose a new model
                  for the segmentation of the objects in such
                  images. The posterior energy is the sum of a prior
                  energy modelling shape and a likelihood energy
                  modelling the image. The prior is a multi-layer
                  nonlocal phase field energy that favours
                  configurations consisting of a number of possibly
                  overlapping or touching near-circular object
                  instances. The likelihood energy models the additive
                  nature of image intensity in regions corresponding
                  to overlapping objects.  We use variational methods
                  to compute a MAP estimate of the object instances in
                  an image. We test the resulting model on synthetic
                  data and on fluorescence microscopy images of cell
                  nuclei. }
}
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