Complete determination of plant tissues based only on auto-fluorescence and the advanced image analysis - study of needles and stamens
2015
Аутори:
Savic, Aleksandar G.Živković, Suzana
Jovanovic, Katarina K.
Duponchel, Ludovic
Kopriva, Ivica
Тип документа:
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт:
Proper determination of tissues is one of the challenging problems in
modern medicine and histology. Currently, interpretation of the results
mainly depends on the experience of a histologist, leading to high
percentage of results misinterpretation. Bearing in mind potential
application, we proposed the set of procedures that allow us to obtain
precise, mathematically determined parameters for tissue discrimination.
First, the method was tested on simulated set of images and compared
with several other algorithms. As the set of experimentally obtained
input data, auto-fluorescence images of needle cross sections
(Piceaomorika) and stamens of common centaury (Centauriumerythraea) were
used. Determination of cell types is based on inherent features of plant
cells - auto-fluorescence. As each cell type consists of various
fluorescent components in different quantities for each type of tissue,
its integral emission spectrum can be used as the fingerprint for
identification. Cross sections were imaged using four sets of filters
for detection of fluorescence (both excitation and emission). Such
filter set is standard equipment for most fluorescence microscopes. One
additional image was transmission image using the same optics. By
applying (0)-norm-constrained nonnegative matrix factorization in a
space induced by explicit feature maps, it is possible to identify up to
11 tissues in needles and five in stamens (actual number of tissues). In
comparison to other image analysis methods, the greatest advantage is
the fact that the number of extracted components significantly exceeds
the number of initial images while most other techniques can extract
only as much components as the number of initial images. Copyright (c)
2015 John Wiley \& Sons, Ltd.
Finding the procedure for proper tissue determination is a challenging
task in various fields of biology and medicine, unfortunately often
affected by subjectivity of the histologist. We have introduced the
method based on (0)-norm-constrained nonnegative matrix factorization
and compared it with several algorithms on controlled, simulated set of
images. The key advantage is the ability to extract much more components
from the starting set of images (often small number) in comparison to
other methods, providing more versatile set of parameters for tissue
classification.
Кључне речи:
image analysis; explicit feature maps; l(0)-norm-constrained nonnegative matrix factorization; fluorescence microscopyИзвор:
Journal of Chemometrics, 2015, 29, 10, 521-527
DOI: 10.1002/cem.2735
ISSN: 1099-128X