zoueu t. jeremie and loum georges laboratoire d’instrumentation image et spectroscopie ( l2is )...
TRANSCRIPT
Séminaire au LISA, 10 Novembre 2009
MULTISPECTRAL LED IMAGING MICROSCOPY APPLIED TO MALARIA DIAGNOSIS AND STUDY
ZOUEU T. Jeremie and LOUM Georges
Laboratoire d’Instrumentation Image et Spectroscopie (L2IS)Institute National Polytechnique Félix Houphouet-Boigny YamoussoukroEmail address: [email protected]
Cote d’Ivoire
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OUR INSTITUTE
Institut National Polytechnique de Yamoussoukro
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OUR BASILICA
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OUR BASILICA
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ACKNOWLEDGEMENTS
TWAS-UNESCO (Third Word Academy of Science), Trieste, Italy
ISP (International Science Programme), Uppsala, Sweden
Atomic Physics Division, Lund University, Sweden
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INTERNAL AND EXTERNAL CONTRIBUTIONS
M. Brydegaard, A. Merdosa, S. Svangberg (Atomic Physics Division, Lund University)
Menan Hervé (Laboratory of Parasitology)
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OUTLINE
Images and Spectroscopy System Overview Malaria Parasites Diagnosis Approach Principal Component Analysis K-means clustering method Conclusion
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IMAGES AND SPECTROSCOPY
Using images data for spectroscopy and diagnosis Selecting specific point or object in images for study Collecting quantitative spectra from images data Identification (or detection) of patterns Grouping points by spectral fingerprint similarities Etc.
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EXAMPLE OF IMAGES
Spati
al dim
en
sion
Spatial dimension
Spectral dimension
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TYPICAL DIMENSIONS FOR OUR APPLICATION
Spatial dimension:Pmn is the pixel intensity for m row and n column
Spectral dimension: Pmn(l) is the intensity of pixel of the coordinates
m and n, for the wavelength l
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TYPICAL DATA IN OUR APPLICATION - 1
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
Spatial dimension
Spati
al dim
en
sion
Spectral dimensionAg21
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TYPICAL DATA FOR OUR APPLICATION - 2
For each wavelength, we have:
)()()(
)()()(
)()()(
npx...
nfx...
n1x
...............ipx...
ifx...
i1x
...............1px...
1fx...
11x
Class 1
Class 3
Class 2200 300 400 500 600 700 800 900 1000 11000
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Longueur d'onde (nm)
Abs
orpt
ion
(u.a
)
Spectre d'absorption d'un Frottis sanguin
300 400 500 600 700 800 900 1000 11000.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
0.52
0.54Spectre d'absorption d'in frottis sanguin
Longueur d'onde (nm)
Abs
orpt
ion
(u.a
)
900 1000 1100 1200 1300 1400 1500 1600 1700
5500
6000
6500
7000
7500
Raman spectrum of hemozoin (Excitation wavelength=514nm)
Wavelength (nm)
Inte
nsity
(ar
bitr
ary
unit)
Clustering
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SPECTROSCOPIC IMAGE: ABSORBANCE IMAGE EXAMPLE
Achieving spectroscopic images by transforming ordinary images into spectroscopic images data
Case of absorbance images : Pa=-Log(Pt-Pb)/(Pref-Pb)
Pa is an absorption image
Pt is transmitted image
Pb is baseline image
Pref is reference image
Idem for reflection and scattering
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MULTISPECTRAL LED IMAGING MICROSCOPY: SYSTEM OVERVIEW
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CASSEGRAIN OBJECTIVE
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LIGHT AND OBJECT INTERACTION
Light Source Sample
Spectrally sensitive detector
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LIGHT AND OBJECT INTERACTION
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EXAMPLE - 1:POLLEN IN BRIGHTFIELD AND DARKFIELD
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EXAMPLE - 2:RING IN TRANSMISSION AND REFLECTION
Transmission image Reflection image
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EXAMPLE - 3:RING IN REFLECTION AND SCATTERING
reflection image Scattering image
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MALARIA PARASITES
Malaria is cause by a intracellular protozoa parasite called plasmodium
When the parasites proliferate into the erythrocytes, the infected RBCs bind to vascular endothelium (cytoadherence) and to the non-infected RBCs (rosetting).
This block the blood flow and leads to high fever, unconsciousness, coma and …
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MALARIA CYCLE
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MALARIA
There are four types of plasmodium: Plasmodium falciparum Plasmodium ovale Plasmodium vivax and plasmodium malaria
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MALARIA
The dangerous and more common (98%) one in Ivory Coast and many African countries is plasmodium falciparum.
The plasmodium falciparum can show about four shapes:
Plasmodium falciparum ring Plasmodium falciparum trophozoite Plasmodium falciparum gametocyte Plasmodium falciparum shizont
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RING FORM
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TROPHOZOITE
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SHIZONT
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GAMETOCYTE
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MULTISPECTRAL LED APPLICATION
Diagnosis: Cheap equipment Gain of time and no need of expertise by using not
marked RBCs Make the thin and thick blood smear more sensitive Automatization of the parasites density and
parasite type measurements Drug-target interaction: Drug target determination Parasite survival strategy determination
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MALARIA DIAGNOSIS APPROACH
200 400 600 800 1000 1200 1400 1600 1800 2000
200
400
600
800
1000
1200
1400
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IMAGE FREE OF BACKGROUND, FOR PARASITES DENSITY AND TYPE DETERMINATION
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SENSITIVITY OF MULTIMODAL EQUIPMENT-1POSITIVE SAMPLE
Transmission image Reflection image
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SENSITIVITY OF MULTIMODAL EQUIPMENT-2POSITIVE SAMPLE
reflection image Scattering image
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SENSITIVITY OF MULTIMODAL EQUIPMENT-3POSITIVE SAMPLE
Transmission image Reflection image Scattering image
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SENSITIVITY OF MULTIMODAL EQUIPMENT-4POSITIVE SAMPLE
Transmission image Reflection image Scattering image
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SENSITIVITY OF MULTIMODAL EQUIPMENT-5NEGATIVE SAMPLE
Transmission image Reflection image
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SENSITIVITY OF MULTIMODAL EQUIPMENT-6 NEGATIVE SAMPLE
Transmission image Reflection image
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SENSITIVITY OF MULTIMODAL EQUIPMENT-7 NEGATIVE SAMPLE
reflection image Scattering image
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CONTRAST FUNCTION DETERMINATION:ABSORPTION SPECTRA -1
300 400 500 600 700 800 900 10000.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85Area Transmission Spectra
(nm)
Op
tic
al d
en
sit
y
in RBC
in RBCout RBC
out RBC
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CONTRAST FUNCTION DETERMINATION:ABSORPTION SPECTRA - 2
300 400 500 600 700 800 900 10000.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85Area Transmission Spectra
(nm)
Op
tic
al d
en
sit
y
PRBC
NPRBC
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PATTERN IDENTIFICATION
We choose a voxel V0 of interest We divide all the voxels of the image by
V0 We calculate the standard deviation of
each voxels We build an image of the standard
deviation values We applied a threshold to the standard
deviation image (SDI)
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RESULTS: ORIGINAL AND SDI
Reference Image Standard deviation Image
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RESULTS: THRESHOLDING THE SDI HIGH DENSITY - PRBCS
Reference Image Threshold Image
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RESULTS: THRESHOLDING THE SDI LOW DENSITY - PRBCS
Reference Image Threshold Image
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PCA APPLICATION
We consider our type data
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MULTISPECTRAL MICROSCOPY AS SPECTROSCOPIC TOOL
9 images taken from 360nm to 1100nm range
Pre-processed images Quantitative absorption extracted PCA applied to the images to reduce
the dimension K-means to cluster the pixels of the
images
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ORIGINAL IMAGE
Example of original image obtained with 600nm
Image length : 110 micrometers longer (640 pixels)
Imag
e w
idth
: 9
0 m
icro
met
er la
rger
(48
0 pi
xels
)
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CROPED INFECTED RBC IMAGES FROM 360NM TO 1100NM
LED 1070 nm LED 970 nm LED 860 nm
LED 660 nm LED 600 nm LED 495 nm
LED 450 nm LED 405 nm LED 377 nm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Séminaire au LISA, 10 Novembre 2009
QUANTITATIVE ABSORPTION
400500
600700
800900
1000
10
20
30
40
50
60
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
(nm)
Coupe transversale ligne N°31
Pixel
Ab
sorp
tio
n
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
10
20
30
40
50
60
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PRINCIPAL COMPONENTS VARIANCE
1 2 3 4 5 6 7 80
10
20
30
40
50
60
70
80
90
Principal Component
Var
ianc
e E
xpla
ined
(%
)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
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PCA FIRST SCORE IMAGE
0
10
20
30
40
50
60
70
80
010
2030
4050
6070
80
-3
-2
-1
0
1
Xposition (pixels)
Scores(:,1) image
Yposition (pixels)
Arb
itrar
y U
nits
-2
-1.5
-1
-0.5
0
0.5
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K-MEANS APPLICATION WITH 7 CLUSTERS
0
10
20
30
40
50
60
70
80
0
10
20
30
40
50
60
70
80
0
5
10
Xposition (pixels)
Kmeans clustering in 7 clusters
Yposition (pixels)
Clu
ster
s nu
mbe
r
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CONCLUSION
Multispectral Imaging can be used as diagnosis and spectroscopy tool
PCA can be used as correlation tool to add information from each spectral channel for malaria diagnosis and study
K-means can be used to cluster the molecular functions in the image
Quantitative spectra can be used to determine contrast function