Toward a Quantitative Analysis of Skin
Lesion Images
B. Caputo (+,**), V. Panichelli (*), G.E. Gigante (**)
(+) Smith-Kettlewell Eye Research Institute,
2318 Fillmore Street, San Francisco
94115 California, USA
(*) Servizio di Fisica Sanitaria, ASL San Giovanni-Addolorata,
via S. Stefano Rotondo 5,
00184 Roma
(**) Physics Department and CISB
University of Roma "La Sapienza",
p.zza A. Moro 3,
00161 Roma
Abstract. Melanoma is the most dangerous form of skin cancer. Skin
cancers, and particularly melanomas, can be easily cured if detected
early. The regularity of the pigment network structure is a significant
sign for early diagnosis of melanoma. We present here a quantitative
analysis of the morphology of the pigment network structure. Our
method is based on the extraction of the pigment network structure
using digital image processing techniques. Once the network is
extracted, it is possible to derive statistical indicators relative to its
regularity or irregularity. We report experiments on a database of 14
images, showing the effectiveness of our method.
1
Introduction
Skin cancers are the most common form of cancers in humans [1]. They can be
classified into melanoma and non melanoma. Although melanomas are much less common
than non melanomas, they account for most of the mortality from skin cancers [1]. If
cutaneous melanoma is detected in its early stages and removed, there is a very high
likelihood that the patient will survive. Unfortunately, the accuracy of clinically diagnosing
melanoma is about 50% [2]. Thus, the importance of developing techniques and methods
that can support the diagnostic process done by the physicians.
Pigmented skin lesions offer several danger signs [1,2]:
 asymmetric shape;

border changes, such as irregularity, satellite pigmentation, erythema, or a
depigmented halo that is irregular;

colour variation: either hyper- or hypo-pigmentation or the spread of pigment onto
normal (flat skin);

diameter enlarged beyond 4 mm;

any new pigmented lesion greater than 6 mm.
The usage of digital image processing techniques for the analysis of medical images
is a well developed area of research [3, 4, 5]. The diagnostic process is a very complicate
procedure, the outcome of which depends on many factors. Some of these factors relate to
the procedure itself and others are associated with characteristics of the human visual
system. In the situation in which all technical factors are at a high quality level, the human
factor becomes more important. The perception of details and the recognition of the
meaning of these details are the weakest links in the biomedical image interpretation
process. Thus the importance of using digital image processing techniques in order to
extract quantitative information from medical images.
In this paper we present a quantitative analysis of the morphology of the pigmented
lesion structure. We make the assumption, supported by many clinical evidences, that
melanomas are characterised by an irregular structure of the pigment network, and non
melanomas by a regular one. We model the network as a set of meshes, and propose a
procedure for computing the number of mashes as well as their area. We expect that the
irregularity of the pigment network should be pointed out by a different statistical
distribution of the density of meshes with an area bigger than the average, regular one. A
first study, made on a database of 14 images, fully confirm our hypothesis and shows the
possibility to provide quantitative statistical indicators that can support the physicians in the
analysis and diagnosis of skin lesions.
To the best of our knowledge, there are no previous studies attempting to provide
quantitative statistical indicator on the regularity of the pigment network structure.
The paper is organised as follows: Section 2 describes in details the proposed strategy
of analysis of the pigment network structure. Section 3 reports experimental results. The
paper concludes with a summary discussion.
2
Materials and Methods
We will assume in what follows that the skin lesion is represented by a digital image.
The modality of acquisition of these images will be described in the next Section. The
quantitative analysis we propose for the characterisation of pigment network structure can
be described in four steps:
1. Pre-filtering. In a pre-processing step, a Wiener filter [6] is applied to each image.
This step has the effect to remove noise from the original image. Then, the image is
enhanced via an histogram stretching operation [6]. This step has the effect to make easily
detectable the network structure.
2. Extraction of the network structure. In order to extract the network structure, we
first re-map the grey levels of the image, then we apply a binary filter on it. Thus, given an
image I = [i(m,n)], m = (1,...,M), n = (1,...,N), i = (1,...G), of dimension M  N and G grey
levels, we re-map each grey level pixel using the equation:
j(m,n) = a[i(m,n) - md(m,n) ] + C
(1)
where md(m,n) is the mean grey level value computed on a square window of size d,
centered on the pixel co-ordinate (m, n). C is a constant representing the grey level value
for which it is possible to detect negative values coming from the squared parenthesis in
equation (1). a is a scaling parameter, usually set to one. This grey level re-mapping helps
to enhance the local variations of the grey level values inside the single mesh. As a
consequence, it is possible to extract the network structure using a binary filter with a
common threshold value > C. An example of the results obtained using this procedure is
shown in Figure 1
Figure 1: An example of network structure extracted by an original image.
The original image (left) is first filtered and binarized (center), then
the false positive meshes are eliminated by hand (right).
3. Computing number and areas of mashes. For each image, we extract all the
meshes, as to say each mesh is mapped on the original image. This allows to have an `on
line' test regarding the correctness of the mesh analysis process. It is also possible to cancel
manually the false positive data. An example is shown in Figure 1.
4. Computing the statistical indicators. The data obtained from each image are
represented via a cumulative histogram [6]. From each histogram, the following statistical
indicators are computed:
 xn ;
n
xn ;

n
 x 
x
2
i
i
i
i
i
i
2
i
x
 x2 
(3)
i
i
Q
i
(2)
.
(4)
where niii is the bin index of the cumulative histogram, and xi is the histogram value in that
bin. Note that the value of Q will depend on how long is the tail of the histogram. An
histogram with longer tail will have a smaller Q value, because of the higher number of
mashes with greater area. Thus, we expect that the histograms representing regular network
structures will have a Q significantly greater than that of networks with irregular structure.
3
Results
In order to test the correctness of the proposed statistical method, we applied it on a
database of 14 images produced by the ``S. Gallicano'' hospital in Rome. All images were
taken using a technique called surface microscopy [2]. The area of interest around the
lesion is coated with a film of oil.
A piece of glass is pressed onto the area, and a colour picture is taken through the
glass with a low-power microscope. Each image was of 512  512 pixels. From an initial
database of 100 images, 14 images were selected by expert dermatologists. Among the 14
selected images, 7 were labeled as regular, as to say representative of regular pigment
network structure, and 7 as irregular, as to say representative of irregular pigment network
structure.
The procedure described in Section 2 was applied to all 14 images: each image was
pre-filtered (step 1), the network was extracted via a binary filter (step 2), the number and
area of meshes was computed for each network (step 3) and finally three statistical
indicators were computed from the histogram of the obtained information (step 4). The
values obtained for the statistical indicators, for all the 14 images, are reported in Table 1
(regular networks) and Table 2 (irregular networks).
Table 1: Statistical indicators for regular nets.
lesion label
reg1
reg2
reg3
reg4
reg5
reg6
reg7
<x>
49.63
21.34
18.39
15.20
27.51
25.77
13.78
< x2 >
99.80
31.53
25.06
20.17
49.61
43.78
19.27
Q
0.49
0.68
0.73
0.75
0.55
0.59
0.71
Table 2: Statistical indicators for irregular nets.
lesion label
irreg1
irreg2
irreg3
irreg4
irreg5
irreg6
irreg7
<x>
82.45
70.87
164.49
142.83
22.93
58.97
147.50
< x2 >
279.36
153.97
1300
607.78
46.49
179.60
389.70
Q
0.29
0.46
0.12
0.23
0.49
0.33
0.38
First, we note that almost all the images classified as regular by dermatologists give a
Q higher than that of those classified as irregular (with just two exceptions, reg1 and irreg5
that both give Q = 0.49). Second, we note that the value < x > is remarkably different for
the two groups. Indeed, for images classified as irregular, the value of < x > is always
higher that that of regular images. Thus, although the experiment was performed on a small
database, results are in agreement with our theoretical expectations, and show the
effectiveness of the proposed method for a quantitative analysis of skin lesion images
4
Summary
In this paper we presented a new statistical method for the quantitative analysis of
pigmented network structure. This method is based on the extraction of the network
structure using digital image processing techniques. Once the network is extracted, it is
possible to derive statistical indicators relative to its regularity or irregularity. We reported
experiments on a database of 14 images; the obtained results show the effectiveness of our
approach.
This work can be extended in many ways. First, we intend to apply this method on
a bigger database of images; second, we plan to apply the method on a database of skin
lesion images with histological examination, in order to study the link between the
statistical indicator values and the melanomic or non melanomic nature of the lesion.
Finally, we intend to study other quantitative parameters useful for the quantitative
description of the pigment network structure. Future work will be concentrated in these
directions.
Acknowledgements
We want to thank Dr. Marmo and the dermatology equipe of the S. Gallicano hospital
in Rome for their cooperation. We thanks A. Capon for useful interactions. This research
work was developed while B. Caputo was at the CISB, University of Rome “La Sapienza”.
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