Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method

Year 2016, Special Issue (2016), 277 - 281, 01.12.2016

Gökçen Çetinel Ali Furkan Kamanlı

https://doi.org/10.18100/ijamec.270453

Cited By: 2

Abstract

 In this
paper, an automatic cell counting method under microscopy is proposed. The cell
counting process can be performed in two ways: The manual counting in which a
specialist counts the cells with naked eye, and the automatic counting that
utilizes the computer-based techniques. In manual counting, there are several
techniques for dying the cells to turn them visible with naked eye. However, if
the concentration is more than normal the cells can overlap. Overlap and
incorrect adjusted microscopy parameters are the main factors that cause
inaccurate counting results. Furthermore, in manual counting inter-observer
variability is high. Even though the same cell image is taken into account by
the different specialist, different counting results can be obtained. Because
of the above mentioned problems, the cell counting process must be performed
automatically.

    The proposed automatic stem cell counting process
is based on image processing techniques that appropriate the frame of method.
At first, stem cell sections were obtained under the fluorescence microscopy.
In the following pre-processing step Gaussian filtering and background
extraction are performed. Before applying watershed algorithm histogram of the
image is partitioned in to four parts and the best combination is determined to
obtain the most exact counting results. The aim of using watershed algorithm is
to make the boundaries and maximum points of the cells more clear. Finally, spherical
contours corresponding to the stem cells are counted.

    The effectiveness of the proposed method is
evaluated by performing numerous computer simulations. It is shown that the
proposed method gives promising results and can eliminate the subjectivity
originated from the manual counting. The method is tested on a database
contains two image groups at different noise levels validated by the
specialists.

Keywords

cell counting, cell detection, pre-processing, embryonic stem cell

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