ZOUEU T. Jeremie and LOUM Georges Laboratoire d’Instrumentation Image et Spectroscopie ( L2IS )...

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


OUR INSTITUTE

Institut National Polytechnique de Yamoussoukro


OUR BASILICA


ACKNOWLEDGEMENTS

TWAS-UNESCO (Third Word Academy of Science), Trieste, Italy

ISP (International Science Programme), Uppsala, Sweden

Atomic Physics Division, Lund University, Sweden


INTERNAL AND EXTERNAL CONTRIBUTIONS

M. Brydegaard, A. Merdosa, S. Svangberg (Atomic Physics Division, Lund University)

Menan Hervé (Laboratory of Parasitology)


OUTLINE

Images and Spectroscopy System Overview Malaria Parasites Diagnosis Approach Principal Component Analysis K-means clustering method Conclusion


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.


EXAMPLE OF IMAGES

Spati

al dim

en

sion

Spatial dimension

Spectral dimension


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


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


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


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


MULTISPECTRAL LED IMAGING MICROSCOPY: SYSTEM OVERVIEW


CASSEGRAIN OBJECTIVE


LIGHT AND OBJECT INTERACTION

Light Source Sample

Spectrally sensitive detector


LIGHT AND OBJECT INTERACTION


EXAMPLE - 1:POLLEN IN BRIGHTFIELD AND DARKFIELD


EXAMPLE - 2:RING IN TRANSMISSION AND REFLECTION

Transmission image Reflection image


EXAMPLE - 3:RING IN REFLECTION AND SCATTERING

reflection image Scattering image


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 …


MALARIA CYCLE


MALARIA

There are four types of plasmodium: Plasmodium falciparum Plasmodium ovale Plasmodium vivax and plasmodium malaria


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


RING FORM


TROPHOZOITE


SHIZONT


GAMETOCYTE


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


MALARIA DIAGNOSIS APPROACH

200 400 600 800 1000 1200 1400 1600 1800 2000

200

400

600

800

1000

1200

1400


IMAGE FREE OF BACKGROUND, FOR PARASITES DENSITY AND TYPE DETERMINATION


SENSITIVITY OF MULTIMODAL EQUIPMENT-1POSITIVE SAMPLE




Transmission image Reflection image Scattering image


SENSITIVITY OF MULTIMODAL EQUIPMENT-5NEGATIVE SAMPLE



SENSITIVITY OF MULTIMODAL EQUIPMENT-6 NEGATIVE SAMPLE



SENSITIVITY OF MULTIMODAL EQUIPMENT-7 NEGATIVE SAMPLE



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


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


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)


RESULTS: ORIGINAL AND SDI

Reference Image Standard deviation Image


RESULTS: THRESHOLDING THE SDI HIGH DENSITY - PRBCS

Reference Image Threshold Image


RESULTS: THRESHOLDING THE SDI LOW DENSITY - PRBCS

Reference Image Threshold Image


PCA APPLICATION

We consider our type data


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


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

)


CROPED INFECTED RBC IMAGES FROM 360NM TO 1100NM

LED 1070 nm LED 970 nm LED 860 nm



0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


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


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%


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


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


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

ZOUEU T. Jeremie and LOUM Georges Laboratoire d’Instrumentation Image et Spectroscopie ( L2IS )...

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