=Paper=
{{Paper
|id=Vol-1638/Paper43
|storemode=property
|title=Development of methods for crystallogramms images classification based on technique of detection informative areas in the spectral space
|pdfUrl=https://ceur-ws.org/Vol-1638/Paper43.pdf
|volume=Vol-1638
|authors=Nataly Kravtsova,Rustam Paringer,Alexander V. Kupriyanov
}}
==Development of methods for crystallogramms images classification based on technique of detection informative areas in the spectral space ==
https://ceur-ws.org/Vol-1638/Paper43.pdf
Image Processing, Geoinformatics and Information Security
DEVELOPMENT OF METHODS FOR
CRYSTALLOGRAMMS IMAGES CLASSIFICATION
BASED ON TECHNIQUE OF DETECTION
INFORMATIVE AREAS IN THE SPECTRAL SPACE
N. Kravtsova1, R. Paringer1,2, A. Kupriyanov1,2
1
Samara National Research University", Samara, Russia
2
Image Processing Systems Institute - Branch of the Federal Scientific Research Centre
“Crystallography and Photonics” of Russian Academy of Sciences", Samara, Russia
Abstract. We propose a new approach to classifying diagnostic crystallograph-
ic images. The classification procedure uses a three-nearest neighbor algorithm
based on the Euclidean distance. The image segmentation is conducted in a spa-
tial domain, with energy values in each segment serving as features. Based on
the value of the separability criterion used in discriminant analysis, most in-
formative features and their respective segments are selected. With the classifi-
cation using only informative segments, the classification error is shown to be
reduced by 2% when compared with the use of the entire image.
Keywords: diagnostic crystallogram, spatial spectrum, discriminant analysis,
k-NN classification.
Citation: Kravtsova N, Paringer R, Kupriyanov A. Development of methods
for crystallogramms images classification based on technique of detection in-
formative areas in the spectral space. CEUR Workshop Proceedings, 2016;
1638: 357-363. DOI: 10.18287/1613-0073-2016-1638-357-363
1 Introduction
Biological fluids may serve as indicators of abnormal metabolic processes associated
with organ pathologies. The fluid composition is representative of metabolism
changes associated with various pathologies. In the case of pathology, there occur
multiple changes in the molecular composition of tissue and biological fluids. One
way to identify the correlation between the constituent elements in a system involves
the phase transition of fluid from one state to another. In the laboratory diagnostics,
the phase state of a biological fluid can be most readily changed by crystallization.
Changes in the properties of the crystals thus obtained are the result of changing phys-
ical-chemical properties of the original biological fluid. Medical crystallograms repre-
sent structures formed as a results of salt crystallization when drying out a biological
fluid. These structures are characterized by key parameters, such as a predominant
direction of crystallization lines and their density at each point in the image. The lach-
Information Technology and Nanotechnology (ITNT-2016) 357
Image Processing, Geoinformatics and Information Security Kravtsova N. et al…
rymal fluid turned out to be the most readily available and fairly informative object
for crystallographic analysis. The crystallographic method of lachrymal fluid analysis
has been recommended as a test for diagnosing inflammatory, tumor, and dystrophic
diseases of the organ of vision [1].
In recent years, computer-aided methods of medical image processing have become
one of key research tools, enhancing the effectiveness of early diagnostics of various
diseases. With an automated analysis, abnormalities in the medical crystallogram
structure can be estimated not just qualitatively but quantitatively as well. In clinical
practice, the crystallograms are analyzed using their photographs. It is not always
possible to identify visually key signs of pathology. This has prompted the use of
methods for digital crystallographic image processing. Advantages of the computer-
ized image analysis include its objectivity and feasibility to conduct a quantitative
image analysis. In the course of image analysis the following problem arises: the
information contained in a crystallographic image is structurally redundant. It has also
been known that if the initial crystallographic image is characterized by parallel lines
of a definite direction, its Fourier transform is also dominated by the same-direction
lines. This property can be put to use when analyzing medical crystallograms [2-5].
2 Description of features used
If the image function and its Fourier transform F(u, v) are considered in a spatial do-
main, then the magnitude |F(u, v)|2 defines an energy spectrum of the image. The
energy spectrum of the image can be directly analyzed as a whole or partially.
In this work, we analyzed features derived by calculating the total energy of a selected
domain of the spectrum image. The spectrum image in the domain of interest was
segmented using a formula:
r2 2
Cr1 r2 12 F r , ,
2
r r1 1
where r u 2 v 2 , θ1 и θ2 – are the bounding angles of the sector (Fig. 1).
Fig. 1. Schematic diagram of the segmentation technique
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Image Processing, Geoinformatics and Information Security Kravtsova N. et al…
Since the spectral image symmetrical relative to the center, then you-division signs
only half of the image will be used to eliminate the signs of recurrence.
3 Implementation of classification and analysis features
In this work, with a view of accumulating statistics we utilized crystallographic imag-
es of a lachrymal fluid. The images were divided in two classes based on a visual
analysis. The training set was composed of 100 samples of 256×256 pixels, contain-
ing 50 images of each class. The images under testing had similar parameters, with
the samples containing 50 images in each class. Based on this data, image classifica-
tion was done. As a criterion of classification quality, we used the classification error,
which defines the percentage of faulty decisions and is calculated by the formula:
m
100% , where m is the number of faulty classifications and n is the general
n
number of the images under testing.
We studied a technique for selecting informative segments in a spectrum image. The
informativeness of segments was evaluated using separability criteria of discriminant
analysis.
The separability criterion for a sample composed of n elements grouped into g classes
and containing p features is given by
J tr ((B W)-1 B) ,
where B is the intergroup scattering matrix, whose elements are found from:
g
bij k 1 nk xik xi x x , i, j 1, p ,
jk j
W is the intragroup scattering matrix, whose elements are derived from:
g n
wij k 1 mk1 xikm xik x jkm
x jk , i, j 1, p ,
xikm is the value of the i -th feature for the m -th element in class k ,
xik 1 nk mk1 xikm is the average value of the i -th feature in class k,
n
xi 1 n k 1 nk xik – is the average value of the i -th feature over all classes,
g
n k is the number of elements in class class k .
For instance, for a set of features whose parameters include 3 rings and 4 sectors (Fig.
1) the following sets were found to be most informative: (sector 1, ring 2), (sector 3,
ring 2). Figures 2−4 illustrate images from each class, their spectra, and most in-
formative spectrum fragments.
In this work, we presented the results of crystallographic image classification using
local features of the spatial spectrum. The classification was done using a three-
nearest neighbor technique based on the Euclidean distance. A minimal error of 6%
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Image Processing, Geoinformatics and Information Security Kravtsova N. et al…
was obtained for feature sets derived by breaking down the image into 4 sectors and
4-8 rings (highlighted in Table 1) [6-8]. Then, discriminant analysis was conducted
[9-11], with the individual separability criterion calculated for each feature. Subse-
quent classification involved the search of features using the following procedure:
A single feature with the maximum criterion was selected, with the classification
done based on that feature;
The feature set was extended by adding one more feature with the second maxi-
mum separability criterion, with the classification done based on two features;
The feature set was extended again by adding the third maximum separability crite-
rion, with the procedure being reiterated until all features had been added.
Figure 5 illustrates in which way the number of features affects the value of error for
the image broken down into 4 sectors and 8 rings.
In our study, the result characterized by the least error was taken as a classification
result following the feature selection (Table 2). The least classification error is high-
lighted in Table 2.
a) b)
Fig. 2. Image examples for each class: (а) class 1 and (b) class 2
a) b)
Fig. 3. Examples of spectrum images for each class: (а) class 1 and (b) class 2
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Image Processing, Geoinformatics and Information Security Kravtsova N. et al…
a) b)
Fig. 4. Images of informative segments for each class: (а) class 1 and (b) class 2
Table 1. Classification error prior to feature selection, %
Number of rings
1 2 3 4 5 6 7 8
1 10 12 11 11 11 11 12 12
Number of sectors
2 9 7 8 8 8 8 7 8
3 9 7 7 7 7 7 7 7
4 9 7 7 6 6 6 6 6
5 7 7 7 7 7 7 7 7
6 8 7 7 7 7 7 7 7
7 8 7 7 7 7 7 7 7
8 7 7 7 7 7 7 7 7
7
Error features, %
6
5
4
3
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
Number of features, %
Fig. 5. Error vs. the number of features
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Table 2. Classification error following feature selection, %
Number of rings
1 2 3 4 5 6 7 8
1 9 10 9 9 9 9 10 10
Number of sectors
2 8 8 7 7 7 7 7 7
3 8 7 6 6 7 7 7 7
4 7 7 6 6 5 5 4 4
5 7 7 7 6 6 6 7 7
6 7 7 7 7 7 7 7 7
7 7 7 7 7 7 7 7 7
8 7 7 7 7 7 7 7 7
4 Conclusion
We have proposed a technique for selecting informative segments in the spectrum
image. The study was conducted using medical crystallogram images. With the entire
spectrum image broken down into segments used for classification, the breakdown
into 4 sectors and 5-8 rings has been found to be optimal, with the classification error
being equal to 6%. The use of the separability criterion borrowed from discriminant
analysis for selecting the informative features has made it possible to reduce the clas-
sification error. For the image broken down into 4 segments and 5-6 rings, following
the selection and use of informative features, the error was reduced from 6% to 5%,
while the breakdown composed of 4 sectors and 7-8 rings allowed the error to be
reduced from 6% to 4%.
The analysis of the results suggests that post-selection informative feature sets enable
a more accurate classification of medical crystallograms of a lachrymal fluid.
Acknowledgements
This work was partially supported by the Ministry of education and science of the
Russian Federation in the framework of the implementation of the Program of in-
creasing the competitiveness of SSAU among the world’s leading scientific and edu-
cational centers for 2013-2020 years; by the Russian Foundation for Basic Research
grants (# 14-07-97040, # 15-29-03823, # 15-29-07077, # 16-57-48006); by the ONIT
RAS program # 6 “Bioinformatics, modern information technologies and mathemati-
cal methods in medicine” 2016.
References
1. Ilyasova NYu, Kupriyanov AV, Khramov AG. Information technologies of image analysis
in medical diagnostics. Moscow: “Radio i svyaz” Publisher, 2012; 424 p. [in Russian]
Information Technology and Nanotechnology (ITNT-2016) 362
Image Processing, Geoinformatics and Information Security Kravtsova N. et al…
2. Paringer RA, Kupriyanov AV. The Method for Effective Clustering the Dendrite Crystal-
logram Images. 9th Open German-Russian Workshop on Pattern Recognition and Image
Understanding “OGRW 2014”, Electronic on-site Proceedings. University of Koblenz-
Landau, 2014.
3. Paringer RA, Kupriyanov AV. The method of forming features for image classification
dendritic crystallograms. Information technologies and mathematical science and man-
agement, Irkutsk, Russia, 2015; 1: 50-55. [in Russian]
4. Paringer RA, Kupriyanov AV. Research methods for classification of the crystallogramms
images. Proceedings of the 12th international conference “PRIP'2014”, Minsk, Belarus,
2014: 231-234.
5. Paringer RA, Kupriyanov AV. Developing improve image quality classification methods
dendritic crystallograms. Collection of scientific works of the international scientific tech-
nical conference “PIT-2014”, Samara, Russia, 2014: 282-286. [in Russian]
6. Kravtsova NS, Paringer RA, Kupriyanov AV. Assessing local features space-
governmental spectrum for classification of texture images. Samara: Proceedings of the in-
ternational youth-term scientific conference “XIII Korolyovskie chteniya”, Samara, Rus-
sia. SSAU, 2015; 2: 134-135. [in Russian]
7. Kravtsova NS, Paringer RA, Kupriyanov AV. Research of algorithms of classification of
texture images based on the analysis of the spatial. Methods of computer diagnostics in bi-
ology and Medicine, Saratov, Russia. Publishing House of Saratov source, 2015: 190-193.
[in Russian]
8. Kravtsova NS, Paringer RA, Kupriyanov AV. Developing technologies detection of in-
formative segment in the spectral region images for classification crystallograms. Collec-
tion of scientific works of the international scientific technical conference “PIT-2016”,
Samara, Russia, 2016: 104-106. [in Russian]
9. Fukunaga K. Introduction to statistical pattern recognition. San Diego: Academic Press,
1990; 592 p.
10. Ilyasova NYu, Kupriyanov AV, Paringer RA. Formation of features for improving the
quality of medical diagnosis based on discriminant analysis method. Computer Optics,
2014; 38(4): 751-756. [in Russian]
11. Ilyasova NYu, Kupriyanov AV, Paringer RA. The Discriminant Analysis Application to
Refine the Diagnostic Features of Blood Vessels Images. Optical Memory & Neural Net-
works (Information Optics), 2015; 24(4): 309-313.
Information Technology and Nanotechnology (ITNT-2016) 363