Hyper-spectral images classification for skin diseases analysis...of the skin, hair and nails. 3 OUTLINE Skin diseases State of the art Projection pursuit and SVM Independent component

Sylvain PRIGENT

Xavier Descombes , Josiane Zerubia

Hyper-spectral images classification for skin diseases analysis

GALDERMA

Hyper-spectral images classification for skin diseases analysis 2

Galderma is a pharmaceutical company specialized in research, development and commercialization of therapeutic solutions, corrective and aesthetic dermatology. It is a global leader in its field of expertise: diseasesof the skin, hair and nails.

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OUTLINE

� Skin diseases

� State of the art

� Projection pursuit and SVM

� Independent component analysis

� Methods comparison

� Conclusion


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� Skin diseases

1- Melasma2- Acne3- Rosacea

Skin diseases


1- Melasma

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© Galderma

Disease showing brown and irregular spots on the face. This disease is caused by a runaway melanocytes in response to a hormonal reaction.


2- Acne

disease characterized by redness and inflammation due to saturation of the pores of the skin by a combination of dead cells and sebum secretion.

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© Galderma


3- Rosacea

Disease characterized by erythema (redness) and chronic tingling in the face. Sometimes, small blood vessels may be visible in the affected areas.

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© Galderma


� State of the art

1- CIEL*a*b space 2- Reflectance measurement3- Absorption spectrum

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State of the art


1- CIEL*a*b space

RGB

CIE L*a*b

a Represents mostly hemoglobin

L Represents mostly melanin

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π180

.*

50*

−=b

LarctgITA

Individual Topology Angle : For melanin quantification

L

a

b

[Stamatas et .al, Pigment cell res, 2004]

© Galderma


2- Reflectance measurement

10

−=

)850(

)(log)( 10 S

SSn

λλ

To quantify the hemoglobin, one can select a q band and normalize it by the band at 850 nm where the hemoglobin influence is low.

[Stamatas et .al, Pigment cell res, 2004]Hyper-spectral images classification for skin diseases analysis

3- Absorption spectrum

baAmelanin += λλ)(

)()()( λλλ melaninc AAA −=

)(*][)(*][)( 111 λελελ deoxyHboxyHbc deoxyHboxyHbA +=

)(*][)(*][)( 222 λελελ deoxyHboxyHbc deoxyHboxyHbA +=

−−−=

)()()()(

log)( 10 λλλλλ

darkref

dark

RR

RRA

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Melanin and hemoglobin concentrations’ estimation : Stamatas algorithm

•Calibration of the measured absorbance (black - white 99%):

•Compensation of the melanin influence whose spectral response is modeled as affine:

•Estimation of hemoglobin concentration by solving a system from the Beer-Lambert law:

[G. N. Stamatas et .al, British Journal of Dermatology, 2008]


� Projection pursuit and SVM

1- Projection pursuit2- SVM classification3- Shading compensation

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Projection pursuit and SVM



13Hyper-spectral images classification for skin diseases analysis

1- Projection pursuit

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Step 1: Partitioning of the spectrum into sub-groups of adjacent bands

Step 2: Projection of each group on a single vector maximizing an interclass distance I.

[S. Mallat et .al, Transaction on Signal Processing, 1993]



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Step 1: Partitioning of the spectrum into sub-groups of adjacent bands

A partitioning in variable size groups is needed:

Solution 1- Fix K and look for the groups boundaries by minimizing the variance of each particular group : [Rellier et .al, Transaction on Geoscience and Remote Sensing, 2004]

Solution 2- Search the boundaries of groups as the significant local maxima:



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Step 2: Projection of each group on a single vector maximizing an interclass distance I.

Kullback Leibler distance is use for I between class i and class j:

2

),(),(),(

ijHjiHjiD kbkb

kb

+= with dxxf

xfxfjiH

j

iikb ∫

=

)(

)(ln)(),(

fi and fj are

the classes pdf

For Gaussian distributions:

( ) ( )2

2)()(),(

1111 IdtrjiD ijjijiji

tji

kb

−ΣΣ+ΣΣ+−Σ+Σ−=

−−−− µµµµ

µ and Σ are the mean and the variance of the distributions.

[Rellier et .al, Transaction on Geoscience and Remote Sensing, 2004]


2- SVM classification

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� Classification into two classes from a linear separator (hyperplane)

Step 1: Determine the separator on a training set

Step 2: Assign a class to each pixel according to its relative position to the separator.

[V. Vapnik, John Wiley and sons, inc.,1998]



optimal Hyperplane:

Dual form:

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� Calculation of the separator:

with

Maximize the margin minimize such as


Dual form:

Kernel:


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� Non linear case: use of a kernel

Gaussian kernel

with



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No detection in areas where there is a shading due to the volume of the face

Requires a compensation of this shading effect

© Galderma

Reconstructed color image Classification without shading compensation


3- Shading compensation

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The last band of the reduced data by Projection Pursuit contains almost only the shading (melanin reacts little to the near infrared)

Shading compensation from the near infrared image



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�Normalization of the reduced data by the last image of this data

Result on the second band of the reduced cube by PP:

Do not compensate all the shading effects

Initial image Image compensated by normalization



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),()(),( jiIRIRMaxji −=Φ

Φ+= zc λλ 0

0

)min()max(

)min()max(

≠

≠

−

−=

IRIRz

λλwith

� Compensation of the reduced data by subtracting the infrared image


Near infrared Near infrared


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Better compensation with the subtraction method

Result on the second band of the reduced cube by PP:

Initial image Image compensated by normalization

Image compensated by subtractions


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Influence of the compensation on the classification by SVM:

© Galderma



Classification with compensation by normalization

Classification with compensation by subtraction


� Independent component analysis

1- The model2- Shading compensation

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Independent component analysis


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Independent component analysis


1- The model

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� Independent component analysis [J.F. Cardoso, Neural Computation,1999]

� Estimation of A by diagonaliziation of the cumulants matrices:

( ) ...2!

)(.)(log)(

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1

+−=== ∑∞

=

tit

n

itkeEth

n

nn

itX σµ

}{}{}{}{}{}{}{}{}{ kjliljkilkjilkjiZijkl ZZEZZEZZEZZEZEZEZEZEZZZZEQ −−−=

}{ jiZij ZZEQ =

� Second order cumulants:

� Fourth order cumulants:

Voxel (i,j)

Mixing matrix

Noise

Quantity of each sources in Xi,j



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� Healty/pathological classification by thresholding the melanin component

© Galderma

Influence of the compensation on the classification by SVM:


Classification with compensation by normalization

Classification with compensation by subtraction


� Methods comparison

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Methods comparison


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Methods comparison


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© Galderma

© Galderma

© Galderma

Reconstructed color image



Methods comparison


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Comparison, over 30 images of the surface calculated by the Stamatas algorithm and thresholding an ICA component compared with on ground truth performed by a dermatologist:

Better match with the ICA method

Methods comparison


Conclusion

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� Conclusion


Conclusion

-Two methods to detect and quantify melasma

Conclusion:

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acknowledgment:

- Galderma for co-funding and providing the data


References

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� J.F. Cardoso, “High-order contrasts for independent component analysis,” Neural Computation, vol. 11, pp. 157–192, 1999.

� S. Mallat and Z. Zhang, “Matching pursuit with timefrequency dictionaries,” Transaction on Signal Processing, vol. 41, pp. 3397–3415, 1993.

� V. Vapnik, “Statistical learning theory,” John Wiley and sons, inc., 1998.

� G. Rellier, X. Descombes, F. Falzon, and J. Zerubia, “Texture feature analysis using a gauss-markov model in hyperspectral image classification,” Transaction on Geoscience and Remote Sensing, vol. 42, pp. 1543 –1551, 2004.

� G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “In vivo measurement of skin erythema and pigmentation: new means of implementation of diffuse reflectance spectroscopywith a commercial instrument.” British Journal of Dermatology, vol. 159, pp. 683–690, 2008.

� G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ.,” Pigment cell res, vol. 17, pp. 618–626, 2004.


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Hyper-spectral images classification for skin diseases analysis...of the skin, hair and nails. 3 OUTLINE Skin diseases State of the art Projection pursuit and SVM Independent component