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. }
}