NUMERICAL ERROR ANALYSIS FOR CONFIGURABLE CELL SEGMENTATION PROBLEM

Year 2019, Volume: 20 , 193 - 205, 16.12.2019

Nataliya Bilous , Oleg Hramm , İryna Ahekian , Abed Thamer Khudhaır Ludmila Illyashenko , Alexander Nerukh

https://doi.org/10.18038/estubtda.650048

Abstract

The current intense interest in gold
nanoparticles is due to their Surface Plasmon Resonances (SPR) that depend
strongly on the shape and size of the nanoparticles. As the SPR wavelength and
resonantly enhanced absorption and scattering properties also depend on the
dielectric medium in which gold nanoparticles are embedded, and also depend on
the way of their clustering, they are useful to design novel nanodevices, in
particular when it is based on ideas taken from nature. With purpose to select
the most promising configurations for novel nanodevice design in this work the
method of cell recognition and evaluation of its efficiency is proposed. Exist
different methods to produce microscopic images, they can be obtained for
different types of cells in different environments. Due to this fact, the
recognition algorithms are needed. All methods have their advantages and
disadvantages and may work well only under certain conditions. Therefore, it is
useful for each specific task to implement a separate algorithm that will be
effective for the existing set of images, and take into account the
peculiarities of these images. The task of this work is not only to develop
flexible and customizable algorithm, that can be configured to segment cells on
different types of images, but also provide numerical error analysis corresponding
to each step of algorithm. As a result, a solution is developed, that has many
customizable parameters to optimize the result for a specific data set and
specific accuracy. In addition, this it is resistant to a lot of noise and
artifacts, that can occur on images, such as uneven background, small debris,
loss of focus when shooting. Numerical error analysis allows getting form of
cell segmentation more precisely to be reproduced for novel nanostructured
device design

Keywords

Cells recognition, Segmentation, Watershed; Hough transform

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