Authors:
Aymeric Histace
1
;
Elizabeth Bonnefoye
1
;
Luis Garrido
2
;
Bogdan J. Matuszewski
3
and
Mark Murphy
4
Affiliations:
1
Cergy-Pontoise University, France
;
2
Universitat de Barcelona, Spain
;
3
University of Central Lancashire, United Kingdom
;
4
Liverpool John Moores University, United Kingdom
Keyword(s):
Image Segmentation, Active Contours, Approximate Entropy, Confocal Microscopy.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
Segmentation of cellular structures is of primary interest in cell imaging for cell shape reconstruction and to
provide crucial information about possible cell morphology changes during radiotherapy for instance. From
the particular perspective of predictive oncology, this paper reports on a novel method for membrane segmentation
from single channel actin tagged fluorescence confocal microscopy images, which remains a challenging
task. Proposed method is based on the use of the Approximate Entropy formerly introduced by Pincus
embedded within a Geodesic Active Contour approach. Approximate Entropy can be seen as an estimator
of the regularity of a particular sequence of values and, consequently, can be used as an edge detector. In
this prospective study, a preliminary study on Approximate Entropy as an edge detector function is first proposed
with a particular focus on the robustness to noise, and some promising membrane segmentation results
obtained on confocal microscopy images are
also shown.
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