SPECKLE MODELIZATION IN OCT IMAGES FOR SKIN LAYERS

SEGMENTATION

Ali Mcheik, Clovis Tauber, Hadj Batatia

IRIT-ENSEEIHT, 2 rue Camichel BP7122, 31017 Toulouse, France

Jerome George, Jean-Michel Lagarde

CERPER, Laboratoires Pierre Fabre, Toulouse, France

Keywords:

Medical image analysis, Statistical approach, Segmentation and grouping, Optical coherence tomography.

Abstract:

In dermatology, the optical coherence tomography (OCT) is used to visualize the skin over a few millimetre

depth. These images are affected by speckle, which can alter the interpretation, but which also carry informa-

tion that characterizes locally the visualized tissue. In this paper, we present a statistical study of the speckle

distribution in OCT images. The capability of three probability density functions (pdf) (Rayleigh, Lognormal,

and Nakagami) to differentiate the speckle distribution according to the skin layer is analysed. For each pdf,

the vector of parameters, estimated over several images which are annotated by experts, are mapped onto a

parameter space. Quantitative results over 30 images are compared to the manual delineations of 5 experts.

Results conﬁrm the potential of the method for the segmentation of the layers of the skin.

1 INTRODUCTION

The diagnosis and the treatment of pathologies of the

skin are largely based on a visual examination by

the dermatologists. This examination requires a great

experience because the skin can present ambiguous

states that are not easily interpretable. Often, as for

the monitoring of cancer, biopsies and histological

analysis are used to resolve these ambiguities. The

development of optical coherence tomography (OCT)

imaging aims at the realization of non invasive optical

biopsies.

OCT images allow the visualization of the struc-

tures of the skin, like the sweat glands, the stratum

corneum, or the change of contrast at the junction be-

tween the dermis and the epidermis. However, the de-

tailed examination of the images is strongly disturbed

by the presence of speckle. The speckle reduces con-

trast and makes difﬁcult the interpretation of the im-

ages. It creates inter and intra variability among the

experts for the identiﬁcation of the borders between

the different layers of the skin. This is particularly

true for tissues with high diffractors density like the

skin. The speckle is thus often regarded as a noise. It

is generally admitted that two types of speckle can be

found in OCT images(Schmitt, 1999; Raju and Srini-

Figure 1: Optical coherence tomography image of the skin

with manual delineations of two layers.

vasan, 2002). The ﬁrst one comes from the interfer-

ence of several reﬂected photons. It appears as pixel-

sized dots with random value that can be ﬁltered via

averaging techniques. The second type of speckle re-

sults from the interferences caused by the retrodiffu-

sions of the propagating waves front within the res-

olution cell of the imaging device. This speckle can

be found everywhere in the image. Several methods

can be found in the literature to reduce the speckle

in OCT images. Among these methods, the angu-

lar and spatial compoundingsigniﬁcantly increase the

347

Mcheik A., Tauber C., Batatia H., George J. and Lagarde J. (2008).

SPECKLE MODELIZATION IN OCT IMAGES FOR SKIN LAYERS SEGMENTATION.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 347-350

DOI: 10.5220/0001086603470350

 SciTePress

signal to noise ratio (SNR) of images and improve the

contours detection(Bashkansky and Reintjes, 2000).

Adaptiveﬁltering techniques are also used to preserve

and reinforce contours between the skin layers, while

reducing the effects of speckle(Iftimia et al., 2003).

However, for a given location and studied tissue, the

speckle has the same characteristics in OCT images.

Even though speckle is generally regarded as a noise,

it is also a source of information for tissue characteri-

zation.

The analysis of the statistics of speckle in each

layer of the dermis and epidermis would facilitate

the differentiation of the skin tissues and thus pro-

vide a model for robust segmentation. This paper is

a contribution to this analysis. It presents a statistical

study of the distribution of the speckle in OCT im-

ages. The sizes and densities of the diffractors in the

visualized tissues characterize the speckle. We mea-

sure these variations to modelize the speckle, by the

estimation of the parameters of three probability den-

sity functions, namely the Rayleigh, Lognormal and

Nakagami distributions.

The remainder of this paper is organizedas follow.

Section 2 describes the models of distribution that we

uses to caracterize the speckle and the respective esti-

mation of their parameters. The experimentations are

detailled in section 3. Finally we draw some conclu-

sions in section 4.

2 SPECKLE MODELIZATION

AND PARAMETER

ESTIMATION

Estimation of the parameters of all three distri-

butions was done using the method of moments

(MM)(Nicolas, 2006).

2.1 Rayleigh Distribution

This model was introduced in a study of speckle

in laser imaging. It supposes a fully developped

speckle, and results from the central limit theorem.

The backscattered signal can be modelized as a pha-

sor sum of the returns from several scatterers within

the resolution cell of the system. The Rayleigh pdf

and cummulative distribution function (cdf) are given

by :

(r) =

−

2σ

r ≥ 0; σ > 0 (1)

(r) = 1−e

−

2σ

(2)

Where σ is the scale parameter.

The estimation of σ by the methods of moments is

given by :

σ =

∑

i=1

(3)

where N is the number of data and x

the data itself.

2.2 Lognormal Distribution

The lognormal distribution has two parameters µ and

σ. Its pdf and cdf are given by :

(r) =

σr

√

2π

−

(logr−µ)

2σ

(4)

(r) = π



logr−µ



(5)

The parameters can be estimated from the calculus

of the ﬁrst two moments :

(

σ =

log

ˆµ = 2log(m

) −

log(m

)

. (6)

2.3 Nakagami Distribution

The Nakagami distribution can modelise the disper-

sion of several backscattered clusters of waves added

incoherently. It includes the Rayleigh distribution as

a special case and can approximate the Rician distri-

bution. The signal to noise ratio of the Nakagami dis-

tribution can take any positive value. Its pdf and cdf

are given by :

(r) =

√

Γ(L)

√

2L−1

−



√



(7)

(r) = Γ

inc

(L,

) (8)

where L is the shape parameter and µ the scale pa-

rameter, with r ≥ 0 and σ > 0. Γ

inc

is the incomplete

gamma function.

As the Nakagami function has two parameters,

moments of order 1 and 2 needs to be calculated. Af-

ter the classic approximation based on the properties

of the Gamma function, the system to calculate the

parameters is given by :

(

L =

√

−1

ˆµ =

√

. (9)

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

348

3 EXPERIMENTATIONS

3.1 Experimental Corpus

The experimental corpus is made of images of the

skin provided by the Laboratory ANONYMOUS. We

used an ISIS SkinDex 300 OCT imaging device. This

device, speciﬁcally dedicated to skin imaging, illu-

minates the skin with 8 LED, emitting a light close

to the infra-red range (1300nm). The 8 diodes are

used simultaneously to recover the signal on 8 par-

allel channels. Shifts in intensity between the vari-

ous channels are at the origin of a phenomenon of

bands which appear on the produced OCT images.

This imaging devicereaches a depth of approximately

900mm. For this study, all the data correspond to

areas of skin located on the front arm. These im-

ages present two layers, successively the epidermis

and the dermis. In the epidermis, the layer of the stra-

tum corneum is a ﬁne irregular band which constitutes

the surface of the skin. The data on which we under-

took our study were submitted beforehand to experts

from the ANONYMOUS laboratory which manually

delimited the external surface of the skin, the stratum

corneum and the junction between the epidermis and

dermis (JED). The observed variability of the delin-

eation testiﬁes the difﬁculty of interpretation of the

OCT images and the need for semi-automatic clas-

siﬁcation methods (Fig. 1). The data provided by

the experts constitute the ground truth used for ex-

perimentations.

3.2 Empirical Data Fitting

The parameters of each distributions are estimated

over the data of the stratum corneum and the data of

the other part of the epidermis delineated by the ex-

perts. Figures 2 and 3 present the ﬁtting of the distri-

butions over the empirical data of each of these layers.

The pdf were scaled so that the area under the curves

matched the total area under the histogram.

On both layers, the best ﬁt was obtained with

the Nakagami distribution. The Rayleigh distribution

leads to the poorer result while the Lognormal distri-

bution goodness of ﬁt is close but less precise than the

Nakagami distribution. This is conﬁrmed by the KS

goodness of ﬁt test that we performed over 30 images

for both the stratum corneum and epidermis layers.

Figures 4 and 5 present the KS values for each distri-

butions.

Quantitative measurements using the KS criterion

show that the nakagami distribution is the most pre-

cise for speckle characterization. It obtains the best

KS scores on each layer, on all the 30 images. We

Figure 2: Fitting of the three distributions over the empirical

OCT data of the stratum corneum.

Figure 3: Fitting of the three distributions over the empirical

OCT data of the epidermis.

Figure 4: KS values of the ﬁts of the stratum corneum data

over 30 images.

performed the experimentations on the separability of

the layers with this distribution.

3.3 Separability of the Skin Layers

We estimated the parameters of the Nakagami distri-

bution over the two layers of the 30 images, for each

of the ﬁve experts. Figure 6 presentsthe vectors of pa-

rameters for each layers, on the 30 images, projected

SPECKLE MODELIZATION IN OCT IMAGES FOR SKIN LAYERS SEGMENTATION

349

Figure 5: KS values of the ﬁts of the epidermis data over 30

images.

onto the parameter space. It shows that the speck-

les that affect the two layers have different caracter-

istics. The two layers can thus be classiﬁed upon the

caracterization of the speckle that affects their corre-

sponding tissue. This conﬁrm both the fact that the

local speckle caracteristics depends on the visualised

tissue, and that this caracterization brings richfull in-

formation for the segmentation of the layers of the

skin.

Figure 6: Parameters vectors of the Nakagami distribution

estimated over 30 images, for both layers.

3.4 Stability of the Estimator

We analysed the KS criterion of the Nakagami distri-

bution on synthetic data samples of various sizes, to

study the stability of the estimator on small samples.

Figure 7 presents the KS values of the Nakagami dis-

tribution calculated for various sizes of the synthetic

data samples.

The results shows the stability of the estimator.

This is relevant for image processing matters, as dur-

ing segmentation or classiﬁcation the speckle has to

be caracterized locally on small data samples.

Figure 7: KS value of the Nakagami distribution for various

sizes of the data sample and several parameters values.

4 CONCLUSIONS

In OCT images of the skin, it is often difﬁcult to dis-

tinguish the various layers and the various lesions.

The statistical study of the distribution of speckle in

OCT images can be the clincher for successful dis-

tinction of these elements. In this paper, we analyzed

the performances of three models of distribution for

the speckle characterization in the stratum corneum

and the remainder of the epidermis. The results show

that the nakagami distribution leads to a better classi-

ﬁcation. The probability density functions that were

studied have one or two parameters. This number is

of primary importance in the capacity of a pdf to pre-

cisely characterize the speckle and differentiate the

layers. We currently work on the study of the Gen-

eralized Gamma distribution with three parameters,

which should produce more precise results.

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