Independent Component Analysis and Raman

Microspectroscopy on Paraffinised Non Dewaxed

Cutaneous Biopsies: A promising Methodology for

Melanoma Early Diagnosis

Cyril Gobinet

, Ali Tfayli

, Olivier Piot

, Valeriu Vrabie

and Régis Huez

CReSTIC, URCA, Campus du Moulin de la Housse,

B.P. 1039, 51687 Reims Cedex 2, France

Unité MéDIAN, CNRS UMR 6142, URCA, 51 rue Cognacq Jay

51096 Reims Cedex, France

Abstract. This paper deals with a promising methodology for melanoma early

diagnosis. Raman spectroscopy is used to record vibrational information of

paraffinised tumoral tissues. Independent Component Analysis (ICA) is

performed on Raman spectra to numerically deparaffinise spectra. Resulting

deparaffinised spectra are used to extract discriminant information specific to

malignant and benign tumors. These spectral specificities can be employed as

molecular descriptors of the type of pathology. A comparison with Principal

Component Analysis (PCA) shows that ICA is more suited to process this kind

of problem.

1 Introduction

Cutaneous melanoma is the most severe form of skin cancers and accounts for three-

quarters of skin cancer deaths. Clinical diagnosis of malignant melanoma is difficult

due to its similarity to atypical benign nevi. Therefore new and efficient non-invasive

tools for early diagnosis of melanomas present a crucial interest in clinical practice.

Since few years, several studies have reported the potential of vibrational

spectroscopies to characterise and to differentiate cancerous from normal tissues.

Raman imaging has been often used due to the fact that Raman spectra provide useful

information about molecular composition of biological structures.

The paraffin embedding process enables to conserve biopsies for several years.

However the use of paraffinised tissues for spectroscopical investigations remains

very restricted. This is due to energetic Raman peaks of paraffin that mask important

vibrational bands of the tissues in recorded spectra. Few works are related to the

analysis of paraffinised tissues, but they led on chemically dewaxed and rehydrated

tissues [1–2], a procedure that may induce alterations in the tissue structure, and

which is time and chemical reagents consuming.

Recently Tfayli et al. [3] have successfully used the FTIR to discriminate between

nevi and melanomas on paraffinised non dewaxed skin sections. The discrimination

was based on narrow vibrational bands where the paraffin has no contribution.

Gobinet C., Tfayli A., Piot O., Vrabie V. and Huez R. (2005).

Independent Component Analysis and Raman Microspectroscopy on Parafﬁnised Non Dewaxed Cutaneous Biopsies: A promising Methodology for

Melanoma Early Diagnosis.

In Proceedings of the 1st International Workshop on Biosignal Processing and Classiﬁcation, pages 19-26

DOI: 10.5220/0001195500190026

 SciTePress

In this work we propose first to numerically deparaffinise the Raman spectra by

using the Independent Component Analysis (ICA). Second, we show the possibility to

extract discriminant sources specific to malignant and benign tumours, sources that

can be employed as molecular descriptors of the type of pathology. We also show that

ICA is a more efficient technique than the commonly used Principal Component

Analysis (PCA) for this kind of treatment.

2 Data acquisition and properties

Tissue sections of 10µm thick were cut from paraffin embedded biopsies

(Dermatology department of Reims university Hospital). Sections were fixed on CaF

slides suitable for Raman analysis. Spectral images were collected by a Labram

spectrometer (Dilor-Jobin Yvon, Lille, France) in a point by point mode with a 10 µm

step. The light source was a titanium-sapphire laser exciting at 785 nm. In each point,

the spectrum was recorded at 1305 wavenumbers covering a spectral region from 200

to 1800 cm

-1

with a resolution of 1.22 cm

-1

Due to the fact that most nevi and melanomas affect the skin epidermis in their first

step of development, the analysis of each tissue is based on the processing of datasets

composed of Raman spectra from the skin epidermis. The malignant melanoma and

benign nevus datasets contain respectively 152 and 119 spectra. Few recorded Raman

spectra are shown on figure 1.

Wavenumber

(

-1

)

Wavenumber (c

-1

)

Fig. 1. Examples of recorded Raman spectra from (1) melanoma and (2) nevus. Peaks labeled

by (*) are associated to the CaF

medium and by (+) to paraffin. The visible part of the keratin

spectrum is labeled by (#).

The analysis of these Raman spectra suggests three remarks:

− whatever is the kind of analyzed tissue, paraffin and CaF

spectra exhibit thin

energetic peaks, which are the predominant features of the recorded spectra; the

contribution of keratin and melanin, which are known to be Raman active species,

is not visible in the recorded spectra;

− recorded spectra are polluted by a so-called background or baseline that originates

from the skin fluorescence;

− due to the spectral resolution of the spectrometer, the thin energetic Raman peaks

of paraffin and CaF

are not aligned on the same wavenumber from a recorded

spectrum to another; source separation techniques as PCA or ICA will fail to

estimate spectra of pure chemical components by computing several neighboring

peaks dispatched in different sources.

Without further processing of data, no information of skin compounds can be

extracted. We are thus investigated methods to extract information related to these

species by examination of statistical and physical characteristics of the dataset.

The first and obvious feature is the instantaneousness data recording because the

scattered light is collected by CCD detectors. Physical laws governing Raman

spectroscopy mechanisms are well known to be linear. Recorded spectra thus result

from a weighted sum of spectra of pure species present in the analyzed tissues. This

instantaneous and linear model is:

∑

saASX

(1)

where X is the data matrix, S is the pure species spectra or sources matrix, and A is

the mixing matrix. Each element a

of A represents the concentration of the j

pure

species s

(which is the j

line of S) into the i

recorded spectrum. The model can

furthermore be written as a sum, as shows the right member of equation (1). The j

column of A, noted a

, represents the concentration profile of the j

pure species. M

denotes the number of sources of the model.

Spectra of the CaF

medium, paraffin and melanin are well known to be sparse and

to possess few peaks localized in narrow bands. Peaks of one of these three species

are not overlapped with peaks of the other species. Mutual independence between

these pure spectra is thus a verified assumption. The keratin spectrum is not sparse

and has not narrow peaks, but its smooth shape makes it to be independent of the

other compounds spectra.

All conditions are combined to apply source separation techniques to the datasets.

3 Methods

To overcome previously cited problems, we propose to process data by a three step

procedure.

The first step consists in suppressing the background. For each recorded spectrum,

it is estimated by a five order polynomial. An asymmetric truncated quadratic cost

function studied by Mazet in [4] is used to estimate the polynomial coefficients. The

processed spectra are obtained by subtracting the corresponding baseline from each

recorded spectrum. An example is given on figure 2.

The alignment of CaF

and paraffin peaks is realized in the second step. This part

consists in upsampling spectra in spectral bands where a peak is localized, in

computing the shift between a reference spectra peak (commonly the first spectrum

recorded on each tissue) and the other spectra peaks, in shifting back the peaks in

order to align their maximums, and finally in downsampling spectra. This alignment

is commonly encountered in geophysical signal processing [5].

The last step corresponds to the elimination of CaF

and paraffin influence. Two

different approaches are possible, the Principal Component Analysis (PCA) and

Independent Component Analysis (ICA). The use of PCA is motivated by its common

application to biological and biophysical datasets [2]. PCA is searching for

statistically decorrelated sources (pure species spectra) that respect the linear model

described above. The decorrelation is only a second order independence, so estimated

spectra may be a linear combination of pure species spectra. In ICA methods [6, 7],

the decorrelation assumption is replaced by the statistical independence of unknown

sources (Raman pure species spectra in our case). This hypothesis is in respect with

the pure species spectra characteristics mentioned in section 2. The JADE algorithm

[7] was used here to estimate the components model. ICA has proved its efficacy to a

wide class of applications [8]. Note that, as usual, application of PCA or ICA is

preceded by centering of data.

Wavenumber (cm

-1

)

Wavenumber (cm

-1

)

Fig. 2. Background removal from a recorded spectrum: (1) estimation of the baseline by a

polynomial of order 5 (dashed line) from a recorded spectrum (solid line); (2) removal of the

baseline by subtraction

4 Results and discussion

4.1 PCA

After the alignment and centering steps, PCA is applied to datasets. The two first

principal components are depicted on figure 3.

Wavenumber (cm

-1

)

Wavenumber

(

-1

)

Fig. 3. The two first principal components estimated on benign nevus

Even if the study of the principal components may lead to discrimination between

tissues, pure species spectra are not well identified. Paraffin and CaF

spectra cannot

be exactly subtracting. It is shown that the first estimated spectrum at the left of the

figure has its peaks well oriented. The second one at the right of the figure exhibits

peaks oriented to opposite directions. This is physically unrealistic. Moreover,

concentration profiles of these principal components exhibit negative and positive

values. To feat to reality, only totally positive concentrations are admitted. Moreover,

the spectra estimated by PCA are still linear combinations of pure species spectra as

can be observed in figure 3 where influence of CaF

and paraffin are mixed.

4.2 ICA – 3 components

This incorrect estimation motivates the use of ICA. The JADE algorithm [7] was used

to estimate for each kind of tissue a three components model predicted by the PCA

analysis. Estimated pure species spectra, corresponding to sources, are depicted on

figure 4 for a nevus.

Wavenumber

(

-1

)

Wavenumber (cm

-1

)

Wavenumber

(

-1

)

Fig. 4. Independent components estimated by a three sources model on the benign nevus

Identical results are obtained for a melanoma. The first source is associated to a

part of the paraffin spectrum. The second source corresponds to another spectral band

characteristic of the paraffin spectrum. The third source is the spectrum of the CaF

medium. A first conclusion is that paraffin acts differently with the underlying tissue

in function of the considered spectral band. A second conclusion is that paraffin and

CaF

spectra are too much energetic compared to keratin or melanin spectra. These

two remarks suggest decomposing spectra with more than three sources. The number

of sources must be sufficient to decompose paraffin in independent behavioral

spectral bands and to estimate the poorly energetic keratin and melanin spectra.

4.3 ICA – 5 components

A four components ICA model lets keratin spectrum mixed with a paraffin source. A

five components model leads to a well decomposition of paraffin (three sources) and

keratin (one source). Nevus estimated independent components are shown on figure 5.

Similar results are obtained for the melanoma. Only the keratin source differs from

one tissue to another. Sources variance is fixed to unity. Paraffin spectrum has been

decomposed in three independent spectral bands. As in the model with three sources,

the CaF

medium conserves its unique Raman peak spectrum. The last source is

similar to the known spectrum of keratin.

Remark 1. Melanin is a priori supposed to be a Raman active species, but its

spectrum is hidden by spectra of paraffin and CaF

. Even if the number of

independent components is increasing, no matching with this spectrum was found.

Remark 2. The accuracy of spectral decomposition by ICA is demonstrated thanks

to the positivity of the estimated mixing matrix. Furthermore concentrations maps can

be organized to show each species repartition in tissue.

The decomposition in several sources of paraffin spectrum does not handicap the

interpretation of results because paraffin is considered as a polluting component in

this application. The interesting information is the keratin spectrum.

(a)

Wavenumber

(

-1

)

(b)

Wavenumber (cm

-1

)

(c)

Wavenumber

(

-1

)

(d)

Wavenumber (cm

-1

)

(e)

Wavenumber (cm

-1

)

Fig. 5. Independent components estimated by a five sources model on the benign nevus. (a), (b)

and (c) independent components associated to paraffin. (d) independent component associated

to CaF

. (e) independent component associated to keratin

Wavenumber (cm

-1

)

Recorded spectrum

Wavenumber (cm

-1

)

Noise subspace

Wavenumber (cm

-1

)

Signal subspace

Fig. 6. Decomposition of initial data space into a noise subspace and a signal subspace. (1) A

recorded spectra lying in the data space. (2) Its noise part lying in the noise subspace. (3) Its

signal part lying in the signal subspace

4.4 Subspace representation

To illustrate the efficacy of numerical deparaffining, let us consider that original data

can be decomposed in two subspaces. The first one is called the noise subspace and is

composed by uninteresting information, e.g. paraffin and CaF

spectra. The second

one, made up by the keratin spectrum, is the signal subspace. It contains useful

information to discriminate the kind of tissue. The original data matrix X can be

written as the sum of the noise subspace X

and the signal subspace X

X = X

+ X

. (2)

Each subspace is defined by:

22332211

CaFCaFparaparaparaparaparapara

sasasasaX

kerakera

saX

(3)

To understand these concepts of noise and signal subspaces, let us illustrate by an

example. A spectrum of the nevus is shown on the left of figure 6. Thanks to ICA, it

is decomposable in two spectra. The one at the middle of the figure is lying in the

noise subspace, while the second at the right in the signal subspace. Note that this last

one is just a scaled version of the spectrum of keratin in figure 5(e). We can notice

that the signal subspace is not very energetic compared to the noise subspace.

400 600 800 1000 1200 1400 1600 1800

-2

-1

Wavenumber (cm

-1

)

Fig. 7. Keratine spectrum estimated on a benign nevus (solid line) and on a malignant

melanoma (dashed-dotted line)

4.5 Keratin spectrum discrimination

A comparison of keratin spectra estimated for a malignant melanoma and a benign

nevus can be done, as shown on figure 7. The sources obtained from ICA show

visible differences between nevi and melanomas. Such differences are visualised with

the changing intensity ratio of the Fermi doublet bands on 850 cm

-1

and 830 cm

-1

for

melanomas it is around 2.5 while it is only 1.6 for the nevi. Such changes could

inform us about the state of the phenylic cycle in the tyrosine residu and the type of

resulting molecular bands (intra- or inter-).

Secondary structure variations are marked by a predominance, in the melanoma

source, of the α helix vibrations (1650cm

-1

) in the amide I band. Similar information

can be obtained from the high intensity of the band on 934 cm

-1

characterising the C-

C stretch in the α helix. On the other hand, the nevi source represents a shoulder band

at 1670 cm

-1

revealing a more important contribution of the β sheet conformation.

The differences in the secondary structure can be quantified by the decomposition

of the amide I band by creating spectral models with Gaussian-Loretzian functions.

The same information can be obtained from the changes of the amide III band, and

from the intensity of the band on 901 cm

-1

5 Conclusion

When a skin sample is paraffinised, the direct analysis of recorded Raman spectra is

not possible because of the predominant intensity of paraffin spectrum over the other

compounds spectra. Thanks to the mutual independence of spectra of these species,

ICA is applicable and estimates physically meaningful sources.

A first conclusion is that paraffin spectrum can be decomposed in three

independent behavioural spectral bands. It means that the underlying tissue is more or

less reacting with some spectral bands of the paraffin spectrum. A second is that

melanin spectrum is not visible when paraffinised tissues are considered because of

the too energetic peaks of paraffin and CaF

. Third, more sources than suggested by

PCA must be employed in order to reveal the low energetic spectrum of keratin.

The last and important conclusion is that estimated keratin spectra of paraffinised

benign nevus and malignant melanoma contain a large amount of information. Little

spectral differences between these spectra lead to the identification of the kind of

analysed tissue. Molecular descriptors of the type of pathology have been found.

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