Josué Alvarez-Borrego

Rosa Reyna Mouriño-Pérez

CiteSeerX

15th International Conference on Pattern Recognition, September 3-7, Barcelona 2000, Vol. 2, Pattern Recognition and Neural Networks,pp 283-286 
Invariant Optical Color Correlation for Recognition of Vibrio cholerae O1 
 
Josué Alvarez-Borrego
1
, Rosa Reyna 
Mouriño-Pérez
1 
1
Cicese, División de Física Aplicada, 
Departamento de Óptica, Km. 107 Carretera 
Tijuana-Ensenada, Ensenada, B. C., México. 
josue@cicese.mx, rmourino@cicese.mx 
 
 
Abstract 
 
 The goal of this work is evaluate the utility of coherent 
optical systems with invariant color correlation for the 
recognition of Vibrio cholerae O1 in culturable and non-
culturable stage stained with direct inmunofluorescence 
in laboratory and environmental samples. Images of 
scenes was recorded with a CCD camera and 
decomposed in three  RGB channels . The position, scale 
and rotation invariant image recognition was made 
through the scale transform. In all cases the bacteria was 
identified. The correlation peaks position that appear in 
green channel output are dependent of differences of 
bacteria’s angle (along y-axis) and size (along x-axis)  in 
problem images with filter image. 
 
1. Introduction 
 
The results reliability of ecological studies about 
microscopic organisms greatly depends on the correct 
identification and quantification of the organism under 
study in natural environments. For instance, Vibrio 
cholerae O1 is responsible of cholera disease and there 
are many unsolved questions about its ecology that could 
be answered through the observation of a large number of 
seawater samples world-wide in order to figure it out
.
[1-
2]. 
The different human-driven techniques to recognize 
bacteria have some troubles in the reliability. The 
automated methods like the optical systems represent a 
new possibility to make better measurements about the 
presence and quantity of this microorganism in its natural 
environment. Automatic systems eliminate observer bias 
and reduce analysis time and relieve researchers of 
tedious activity of organism identification and counting 
and provides major effectiveness. 
Nevertheless, bacteria’s recognition of particular specie 
is a complex issue. Bacteria shape does not provide 
enough information to identify them, because there are 
many species that share the same shape. Vibrio cholerae  
Gabriel Cristóbal-Pérez
2
, José Luis Pech-
Pacheco
1,2
. 
 
2
Csic, Imaging & Vision Dept. 
Serrano 121. 28006 Madrid, Spain. 
gabriel@optica.csic.es, pech@optica.csic.es 
 
 
 
 
O1 has a transparent curve rod the same as all kind of 
Vibrionaceae family among others[3]. To solve this 
problem, we can mark Vibrio cholerae O1 with 
monoclonal antibodies and give them a specific green 
color[4]. We use optical color correlation systems in order 
to increases the discriminability capacity of pattern 
recognition filters, taking in account color and shape 
information[5-8]. Bacteria’s color and shape depends on 
the illuminating wavelength, that is, color introduces 
additional information for recognition effectiveness. The 
logical or arithmetical sum of polychromatic object 
decomposition in three simple monochromatic channels 
(Red, Green and Blue RGB) produces a high level of 
recognition of targets.  Bacteria’s morphology, orientation 
and size changes are other problems that we have to 
address. Besides shape and color, we need an optical 
system invariant to position, scale and rotation. We use 
the digital scale transform, a particular case of the Mellin 
transform[9] in order to implement an optimal process 
that guarantees a high discrimination capability of 
invariant pattern recognition through color correlators. 
This approach have never been used in color systems.  
Our goal is to evaluate the efectiveness of color 
correlation sistems for Vibrio cholerae O1 recognition, 
and also develop invariant filters comparing the 
performance of Matched Filters with Phase-Only filters. 
 
 
2. Invariant color object recognition 
 
     A numerical simulation was performed in order to 
correlate Vibrio cholerae O1 with Phase-only filters 
(POF).   All steps were developed digitally. The Scale 
transform was used in this correlation process due its 
invariance to size changes[10].  If we call c the scale 
variable then scale transform and its inverse is given by 
 
15th International Conference on Pattern Recognition, September 3-7, Barcelona 2000, Vol. 2, Pattern Recognition and Neural Networks,pp 283-286 
dx
x
e
)x(f
2
1
)c(D
0
xlnjc

 

  ,                     (1) 
0x;dc
x
e
)c(D
2
1
)x(f
xlnjc


 


 .          (2) 
The scale transform can be written as 
,dxx)x(f
2
1
)c(D
0
2/1jc




                           (3) 
which shows that it is the Mellin transform with the 
complex argument –jc+1/2.   The Fourier-Mellin 
transform was introduced by Casasent[11] for rotation 
and scale invariant pattern recognition and also Altes has 
used it to study mammallian hearing[12].  A practical 
realization of the Mellin transform is given by a 
logarithmic mapping of the input scene followed by a 
Fourier transform.   Since we are dealing with the Mellin 
transform of a complex argument, there exists a direct 
relationship with the Fourier transform.  In particular if 
we define a signal )x(lnf
x
1
)x(f l   then by 
substituting in Eq. 1 we have 
 
,dxe)x(f
2
1
)c(D jcxl 




                               (4) 
that is )c(D)c( lF .   From this relation one can see 
the scale by resampling the uniformly distributed samples 
of x with a logarithmic function. 
A non-separable direct 2D scale transform )c,c(D r  is 
used in this correlation process because it is invariant to 
size changes, and is given by: 
 
,drder),r(f
2
1
)c,c(D
jc
2
0
2/1jc
0
r
r 







 (5) 
and taking the log of the radial coordinate )rln( we 
get 
.dde),(fe
2
1
)c,c(D
)cc(j
2
0
2/
0
r 

 




   
                                                                                     (6) 
     As it was mentioned before, bacteria’s recognition of 
particular species is a complex issue. Bacteria shape does 
not provide enough information to identify them, because 
there are many species that share the same shape. In 
specific case of Vibrio cholerae O1, color information is 
so important and color becomes an important variable, 
which we need to add it to this mathematical process of 
identification. 
     In general, a polychromatic object presents different 
shape and amplitude distribution Ai(x,y) when 
illuminated with different wavelength . However, two 
different objects may present similar amplitude 
distribution when they are illuminated with determined 
wavelength 0. So, in an optical pattern recognition 
process using a correlator illuminated with a wavelength 
0, these objects will give very similar amplitude 
correlation distributions, and some false alarms will 
appear. To avoid this problem, it is necessary to use the 
information about the dependence of the object amplitude 
distributions on wavelength[13]. 
     Most of the natural colors can be obtained as a 
combination of three colours (called primaries) if they are 
well selected. Each of them has to be on red (R), green 
(G), and Blue (B) regions of visible spectrum 
respectively. When the object is a transparency, the 
amplitude transmittance obtained with illumination of one 
of these primaries will be called red, green and blue 
components of the object. 
     The recognition of an object in a scene is achieved by 
decomposing the information in three monochromatic 
channels and by identifying the object independently in 
each channel. In another words, the correlation Ci(x,y) 
between the scene to be analyzed Fi(x,y) and the bacteria 
to be detected Bi(x,y), in this case, are obtained by 
illuminating an optical set up with three wavelengths 
i=R,G,B which cover visible spectrum 
 
)y,x(B)y,x(F)y,x(C iii    (7) 
 
     Thus, the invariant correlation will be repeated for 
each channel (R,G, and B).  In each channel, the filter to 
be used is matched to the corresponding component of the 
target.  In general, objects which have a determined 
component Ai(x,y) similar to component of the target 
Bi(x,y) will give a maximum of correlation in this 
channel (i). But only the target will simultaneously give 
a correlation maximum in each channel. So, an object is 
detected as the target if it simultaneously produces a 
correlation peak in the three channels.  But, in this 
particular case of Vibrio cholerae O1, we could mark it 
with a specific green color with monoclonal antibodies. 
So, the correct identification for these bacteria will be in 
the green channel, the presence of correlation peaks in the 
other two channels is a discriminate criterion.  Therefore 
it is possible, with this methodology mentioned above, to 
15th International Conference on Pattern Recognition, September 3-7, Barcelona 2000, Vol. 2, Pattern Recognition and Neural Networks,pp 283-286 
recognize Vibrio cholerae O1 in a digital correlator 
invariant to rotation, position and scale. 
 
3. Results and discussions 
 
 
(a) 
050100150200250
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
 
(b) 
 
(c) 
 
Figure 1. Correlation of three Vibrio cholera 01 with 
different scale. 
 
(a) 
 
 
(b) 
       
       Figure 2.  Correlation of three Vibrio cholera 01 with 
different rotation. 
 
Figure 1 shows an invariant correlation, in the green 
channel, where it is possible to identify three Vibrio 
cholerae 01 with three different scales.  Figure 1a shows a 
tridimensional perspective of the results where we can 
observe three peaks (in the scale direction). Each peak 
corresponds to one identification.  Figure 1b shows a 
profile of the first line (filled line) and of the second line 
of the output matrix (dotted line).  In this way we can 
observe better the peaks.  Figure 1c shows the problem 
image and the filter to be recognized.  Figure 2 shows the 
same filter but with different problem image.  Now we 
can see three different rotations.  The output plane, in the 
green channel, shows three peaks which corresponds to 0
o
 
degrees, 90
o
 degrees and 180
o
 degrees of rotation with 
respect to the filter.  In the red and blue channel we can 
not find any information. 
 
4. Conclusions 
 
Using the scale transform via Mellin transform is 
possible to identify Vibrio cholerae 01 to different scales 
and rotations.   The ubications of the peaks depend of the 
scale and rotation of the images to be recognized.   The 
output correlation is in the green channel only. 
15th International Conference on Pattern Recognition, September 3-7, Barcelona 2000, Vol. 2, Pattern Recognition and Neural Networks,pp 283-286 
 
5. References 
 
[1] R. Colwell, “Global climate and infectious disease: The 
cholera paradigm”, Science,   274:2025-2031, 1996. 
 
[2] R. R. Mouriño-Pérez , “Oceanography and  the seven cholera 
pandemic”, Epidemiology, 9(3):355-357, 1998. 
 
[3] K. Wachsmuth, G. K. Morris, J. C. Feeley, “Vibrio” In 
Manual of Clinical Microbiology, pages 226-234, Denver, CO, 
1980. 
 
[4] J. A. K. Hasan, D. Bernstein, A. Huq., L. Loomis, M. L. 
Tamplin, R. R. Colwell, “Cholera DFA: An improved direct 
fluorescent monoclonal antibody staining kit for rapid detection 
and enumeration of Vibrio cholerae 01”, FEMS Microbiol 
Letters, 120:143-148, 1994. 
 
[5] M. S. Millan, J. Campos, C. Ferreira, M J. Yzuel, “Matched 
filter and phase only filter performance in colour image 
recognition”, Optics communications, 73(4):277-284, 1989. 
 
[6] M. S. Millan, M. J. Yzuel, J. Campos, C. Ferreira, 
“Strategies for the color character recognition by optical 
multichannel correlation”, In SPIE, volume 1507,  pages 183-
197, 1991. 
 
[7] M. S. Millan, N. Vila, M. J. Yzuel, “Colour reversal films in 
multichannel optical correlators for polychromatic image 
recognition”, Pure Appl. Opt. , 1:199-218, 1992. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
[8] V. Kober, V. Lashn, I. Moreno, J. Campos, “Color 
component transformations for optical pattern recognition”, J. 
Opt. Soc. Am., 14:2656-2669, 1997. 
 
[9] J. L. Pech-Pacheco, J. Alvarez-Borrego, “Optical-digital 
system applied to the identification of five phytoplankton 
species”, Marine Biology, 132:357-365, 1998. 
 
[10] L. Cohen,  “Time-frequency analysis”, Prentice Hall Signal 
Processing Series, Upper Saddle River, New Jersey 07458, 
1995. 
 
[11] ] D. Casasent and D. Psaltis.  Position, rotation and scale 
invariant optical correlation. Appl. Opt., 15(7):1795-1799, 1976. 
 
[12] R. Altes.  The Fourier-Mellin transform and mammalian 
hearing. J. Acous. Soc. Am., 63(1):174-183, 1978. 
 
[13] J. Campos, M. S. Millan, M. J. Yzuel, C. Ferreira, “Colour 
invariant character recognition and character-background colour 
identification by multichannel matched filter”, In SPIE, volume 
1564, pages 189-198, 1991.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  


Invariant Optical Color Correlation for Recognition of Vibrio Cholerae

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=E085DBF89BAED428DC4F937E79C0E385?doi=10.1.1.1056.1329&rep=rep1&type=pdf

Invariant Optical Color Correlation for Recognition of Vibrio Cholerae

Abstract

Similar works

Full text

Available Versions

CiteSeerX