Engineering, 2013, 5, 482-486
http://dx.doi.org/10.4236/eng.2013.510B099 Published Online October 2013 (http://www.scirp.org/journal/eng)
Copyright © 2013 SciRes. ENG
Contour Extraction of Skin Tumors Using Visual
Attention and GVF-Snake Model*
Li Ma, Tianzhen Su
1College of Life Information & Instrument Engineering, Hangzhou Dianzi University, Hangzhou, China
2School of Automation, Hangzhou Dianzi University, Hangzhou, China
Email: email@example.com, firstname.lastname@example.org
Contour extraction of skin tumors accu rately is an important task for further feature generation of their borders and sur-
faces to early diagnose melanomas. An integrated approach, combining visual attention model and GVF-snake, is pro-
posed in the paper to provide a general framework for locating tumor boundaries in case of noise and boundaries with
large concavity. For any skin image, the visual attention model is implemented to locate the Region of Interests (ROIs)
based on saliency maps. Then an algorithm called GVF-snake is utilized to iteratively drive an initial contour, deriving
from the extracted ROIs, towards real boundary of skin tumors by minimizing an energy function. It is shown from ex-
periments that the proposed approach exceeds in two aspects compared with other contour-deforming methods: 1) ini-
tial contours generated from saliency maps are definitely located at neighboring regions of real boundaries of skin tu-
mors to speed up converges of contour deformation and achieve higher accuracy; 2) the method is not sensitive to nois-
es on skins and initial contours extracted.
Keywords: Visual A ttention; GVF-Snake; Contour Extraction; Skin Tumors
Melanoma is known as one of the most malignant skin
tumors probably appearing on any parts of human bodies.
Clinical studies show that early diagnosis can effectively
improve the cure rate. In CAD systems, identifying and
extracting ski n tumors accurately is the important stage
as the further ABCD diagnosing rules , A(asymmetry),
B (border irregularity), C (color) and D (diameter), are
highly depended on the extracted tumor contours or ROIs.
Extractions of skin contours, especially for melanomas,
are considered as a challenging t ask as they appearing
variety in color surfaces and geometrical irregularity
along the tumor contours. There are many research works
in literatures on contour extractions for skin tumors.
Among them, a popular approach is the active contour
models called Snakes  based on contour evolutions
towards the optimal positions by minimizing its energy.
The disadvantages of the Snakes algorithm are: 1) the
initial contour should be provided manually; 2) defor-
mable contours might not reach the object boundaries in
case of weaker gradients in images. To overcome the
influences of weaker gradients, an algorithm of GVF-
snake  is presented by introducing a novel external
force, Gradient Vector F low, to generate a distributed
force field driven deformed contour towards object
boundaries with large concavity. Unfortunately the initial
curve is still needed by users for either GVF -snake or
ASM  and converges of deformed contours are sensi-
tive to initial curves. There are some modified versions
on initial contour generation but no general framework
for reliable format ion of initial contours [5,6].
In recent years, visual attention model becomes a hot
research field in video monitoring systems to identify
interested objects from visual information of environ-
ments. It is a process of selecting information based on
saliency in images (bottom-up) and on task-related prior
knowledge (up-down). The advantage of the model is
that it could drive attention to focus on candidate objects
even only in bottom-up module (data-driven). The moti-
vation of the pap er is to b ui ld a general model for
automatically extracting contours of skin tumors with ro-
bustness and accuracy. In this paper an integrated
approach of visual attention model and GVF-snake are
proposed to perform automatic boundary extraction of
skin tumors. The proposed algorithm is illustrated in
Figure 1 where dashed block indicates sub-task of the
system. It is shown in Figure 1 that t here are three sub-
blocks in the proposed algorithm for the contour extrac-
tion. The first one refers to visual attention based ROI
*This work is supported by National Scientific Foundation of China
L. MA, T. Z. SU
Copyright © 2013 SciRes. ENG
Feature contrast maps
Saliency maps & ROI
ROI extraction using
Initial Snake curve
Figure 1. The proposed method for contour extraction of
extraction, and then GVF field is calculated to present
driven force map of the input image. Finally initial
contour from the extracted ROI is iteratively evolved
using GVF-snake model to locate the actual boundaries
of skin tumors based on the criteria of energy minimi-
2. Bottom-Up Saliency Map Formulation
2.1. Visual Attention Mechanism
The model of bottom-up salient region selectio n pre-
sented by Itti-Koch is based on center-surround mechan-
ism in bottom-up way . Firstly, the input image is
processed progressively by Gaussian low-pass filter and
then sub-sampled to generate the pyramids of multi-fea-
ture maps with nine spatial scales, where Layer 0 is an
original image and the length and width in next layer is
reduced by half than the previous layer. Each feature,
such as intensity, color and orientation in general, is then
computed by a set of linear “center-surround” operations
akin to visual receptive fields. Center-surround is im-
plemented in the model as the difference between fine
and coarse scales: The center is a pixel at scale
, and the surround is the correspond ing pixel
. So the feature con-
trast map, the region should be paid attention to, is gen-
erated by interpolation to the f iner scale and point-by-
point subtraction between center and surrounding layers.
2.2. Saliency Maps and ROI Generation
For images of skin tumors, color and intensity are key
visual features in human vision while orientations are
less-relevant to identifications of skin tumors. In particu-
lar, 6 for intensity features and 12 for color features from
both RG and BY channel of color differences . After
the 18 feature contrast maps are formed, the normaliza-
tion is taken for each feature contrast maps. Then feature
maps in either color and intensity sub-group are com-
bined into two conspicuity maps in scale four:
intensity (Equation (1)) and
for color (Equation (2)).
CRGc sBYc s
” is defined as across-scale addition. It is im-
plemented by reduction of each map to scale fou r and
As for the final saliency map
, it is ineffective using
conventional method based on the linear combination of
the two conspicuity maps as malignant tumors with rough
surfaces in intensity space and variety in color space are
quite different from common objects with even surfaces.
So a nonlinear operator is proposed in the paper for the
final fusion of saliency map for objects with uneven sur-
faces. The saliency value at each pixel
The generation of saliency map based on visual atten-
tion is presented in Figure 2, where Fig ur e 2(a). is an
original image of a melanoma with uneven color distri-
butions and irregular boundary. The conspicuity maps,
darker region referring to higher attention area, for both
intensity and color are represented in Figu re 2(b) and
Figure 2(c) respectively. Fig ur e 2(d) is the final saliency
map using Equation (3). It is noted from the figure that
color saliency map wo rks better than intensity one.
At last, ROIs would be further extracted from saliency
map. The procedure of ROI extraction is described as
1) Threshold the saliency map to get a binary image.
Figure 2. Formation of the saliency map of a melanoma. (a)
Original image; (b) Color channel; (c) Intensity channel; (d)
The saliency map.
L. MA, T. Z. SU
Copyright © 2013 SciRes. ENG
2) Take the opening operation to remove some noises.
3) If there are several ROIs, then the ROI candidates
are marked and the one with the largest area is chosen to
be the final ROI.
3. Contour Evolution by GVF-Snake
Snakes , also active contours, firstly proposed by Kass
are deformable curves within an image domain that can
move under the influences of internal forces, coming
from and within the curve itself, and external forces
computed from the image data. The curve evolution un-
der both internal and external forces is mathematically
described as a process of energy minimization in Equa-
refers to a deformable curve energy,
represents internal forces and
corresponds to ex-
3.1. Gradient Vector Flow Snake
In the traditional Snakes,
is computed by the nega-
tive gradient of the input image (
Although it is simple and easy to be implemented, there
are two key drawbacks both on setting an initial curve
close to the true boundary and hardly converging to
boundary concavity. In order to solve these difficulties,
GVF  method is proposed by Xu Chenyang and Prince
in 1997. In this model, a new version of external forces
called gradient vector flow (GVF) fields is presented.
These fields are dense vector fields derived from images
by solving a pair of decoupled linear partial differential
equations which diffuses the gradient vectors of a binary
edge map of the image to minimize an energy func tion.
1) GVF formation. For any gray-scale image
its GVF is defined as
( )( )( )
,,, ,V xyuxyvxy=
represen ts the coordinate. Then an energy func-
is defined from the GVF field and the binary
is a regularization parameter governing the
tradeoff between the first and the second term.
GVF field could be calculated by solving the Euler
equations of Equation (5) to minimize the energy func-
is the Laplacian Op-
should be set according to the amount of
noise present in the image (more noise, higher
2) GVF-snake. The active contour that us es the GVF
field as its external force is named as a GVF-Snake. It
effectively expands the scope and intensity of the exte r-
nal force. As for GVF -s nake considered, external force
in the energy functional
 (Equation (7)) is replaced with
( )( )( )
E stx stxstE
is the ith pixel on the current contour.
The contour evolution is iteratively performed using Eq-
uation (8) by minimizing the Equation (7).
where, the matrix
is a pentadiagonal banded
matrix related to parameter
in Equation (7) . As
mentioned above, the curve profile could move into the
concave boundary regions because there are stronger ex-
ternal forces but still needs setting initial curves.
3.2. Contour Extraction of Skin Tumor s Based
on Visual Attention and GVF-Snake
As mentioned above, GVF-snake provides the solution
for one of major defects in conventional snake with re-
spect to contour concavity, but its initial contour is still
given by users. In this way the final curves may not be
guaranteed to reach the actual boundaries of objects es-
pecially when initial contours are located at the weaker
regions of GVF fields. However, visual attention based
ROI extraction would make contribution to initial con-
tour generation automatically and ensure the contour
derived from saliency map to locate at neighboring re-
gions of real object boundary. The proposed algorith m of
combination of visual attention and GVF-Snake for skin
contour extractions is described below:
Step 1. Visua l a ttention is used for the input image to
obtain the ROI of a skin tumor from a saliency map (see
the Section 2).
Step 2. The GVF field of input image is generated using
the Equation (6) where edge gradient
can be calculated from the gray scale image
Step 3. The initial contour
tracted from the contour of ROI obtained in step1.
Step 4. The initial contour is iteratively evolved based
on Equation (8) whic h moves the deformable contour
towards the real contour of skin tumor in the image. Ite-
ration isn’t stopped until the most of points on the con-
tour are not change. Then the final result of contour ex-
L. MA, T. Z. SU
Copyright © 2013 SciRes. ENG
traction is obtained.
4. Experimental Results
To verify the effectiveness of our proposed method for
skin contour extraction, sample images were all down-
loaded from the Dermnet Skin Disease Image Atlas da-
tabase at website http://www.dermnet.com. Each image
was of size 300 × 200 pixels and working platform is
matlab 7.11 on Windows 7. The proposed method in this
paper is compared with other approaches (GVF-snake
4.1. Experiments of Contour Extraction for Skin
Experiments are taken on three aspects: sensitivity on
initial contour, robustne ss from noises and accuracy
measure. Only melanomas are chosen from the database
as accurate extraction of their boundaries is more di ff i -
cult than moles.
In Figure 3 comparison a mong severa l methods is
presented for a given image of a melanoma wh ere curve
marked with dashed thin line (“x···x”) in white indicates
the initial contour and the one marked with “—” in red
represents the final bo und ary of the skin tumor. The
contour extracted on the proposed algorithm is given in
Figure 3(a) where the initial curve generated fro m visual
attention is located in the internal region of the tumor and
the final contour reaches the real boundary of the tumor.
The results of other methods, ASM and GVF-snake, are
shown in Figure 3(b), Figure 3(c) and 3(d) respectively.
As for ASM method, the initial contour in Figure 3(b)
derived from the averaged boundary in database of skin
tumors and it is noted that the final contour fails to reach
the real boundary as the range of contour deforming is
limited by its model parameters. The GVF-snake is
Figure 3. Comparison results of contour extraction on dif-
ferenrt mothods, (a) proposed method; (b) ASM algorithm;
(c) GVF-snake; (d) GVF-snake.
sensitive to the initial contour setting when given two
different initial contours (see Fig ures 3(c) and (d)).
The experiment in Figure 4 is for testing and compar-
ing methods above when noise exists. In Figure 4, there
are some hairs around a skin tumor. It is shown that the
final converged contour (Figure 4(a)) is located at actual
positions of the tumor boundary using proposed method
while either ASM (Figure 4(b)) method or GVF-snake
(Figures 4(c) and (d)) have poor results. It is the hairs
(noise) that interfere with moving direction of ASM and
GVF field fo r contour deformations. It is known from
experiments that the proposed approach (visual attention
& GVF-snake) performs better than the other two. It is
ensured that the initial contours from visual attention
model are located near boundaries of skin tumors as the
region of boundaries with strong feature contrast would
have larger values of saliency.
4.2. Accuracy Measurement
Accuracy of contour extractions is an important index for
algorithm evaluation. Apart from conventional accuracy
measure, another accuracy measure is given in the paper
using binary logi c s .
is the accuracy index,
ing the area of a region.
is the region surrounded
by an actual contour marked by users and
sents the region surrounded by the final contour extracted.
compl e t e the operatio n of XOR.
The experimental results are given in Table 1 where
accuracy and executing time are compared. Ten sample
images are selected randomly from sub-database of me-
lanomas. There are three methods, VA& GVF-snake,
GVF-snake and ASM used in the experiments. For the
Figure 4. Comparison results for contour extraction on
different mothods. (a) Proposed method; (b) ASM algo-
rithm; (c) GVF-snake; (d) GVF-snake.
L. MA, T. Z. SU
Copyright © 2013 SciRes. ENG
Table 1. Performance comparisons.
Accuracy VA & GVF-Snake GVF-Snake ASM
Average 91.145% 74.322% 55.441%
Minimum 86.197% 20.615% 36.35%
Executing time 3.523s 4.095s 34.472s
accuracy measure in Equation(9), two iterms, both aver-
aged value of
and its minimum in the testing group
It is shown in Table 1 that: 1) the best performances
on extraction accuracy are achieved by the proposed
method in this paper; 2) the executing time of the VA &
GVF-Snake is the least among the three methods. And it
is worst for ASM on both accuracy and executing time.
Model based contour extraction of skin tumor faces dif-
ficulties in initial contour setting as it may be located at
weaker gradient fields and failed to reach real boundary
of a tumor. The proposed method combines visual atten-
tion model with GVF-snake to provide a solution for the
problems above. The visual attention, using feature con-
trast based saliency maps, generate an in itial contour n ear
the ideal bounda ry. Then it is further moved by GVF-
snake to secure converges to real border of a tumor. It is
indicated from the experiments that the proposed method
in this paper has three advantages over ASM model and
GVF-snake (both are sensitive to initial contours): 1)
initial contours are guaranteed to be near real boundaries
to make the process of curve evolution faster with higher
accuracy; 2) experimental results are not sensitive to
noises; 3) In itial c ontour is generated automatically. The
method is suitable for contour extractions of objects
which have large concavity or noises exist in the back-
ground of images.
 I. Maglogiannis, S. Pavlopoulos and D. Koutsouris, “An
Integrated Computer Supported Acquisition, Handling
and Characterization System for Pigmented Skin Lesions
in Dermatological Images,” Information Technology in
Biomedicine, Vol. 9, No. 1, 2005, pp. 86-98.
 M. Kass, A. Witkin and D. Terzopoulos, “Snakes: Active
Contour Models,” International Journal of Computer Vi-
sion, Vol. 1, 1998, pp. 321-331.
 C. Xu and J. L. Prince, “Snakes, Shapes and Gradient
Vector Flow,” IEEE Transactions on Image Processing,
Vol. 7, 1998, pp. 359-369.
 T. Cootes, “An Introduction to Active Shape Models,”
Image Processing and Analysis, 2000, pp. 223-248.
 C.-C. Liu, C.-Y. Tsai, T.-S. Tsui and S.-S. Yu, “An Im-
proved GVF Snake Based Breast Region Extrapolation
Scheme for Digital Mammograms,” Expert Systems with
Applications, 2011, pp. 1-6.
 B. R. Wu, M. Xie, G. Li and J. J. Gao, “Medical Image
Segmentation Based on GVF Snake Model,” IEEE Con-
ference on Second International Intelligent Computation
Technology and Automation, Vol. 1, 2009, pp. 637-640.
 L. Itti, C. Koch and E. Niebur, “A Model for Saliency-
Based Visual Attention for Rapid Scene Analysis,” IEEE
Transactions on Pattern Analysis and Machine Intelli-
gence, Vol. 20, No. 11, 1998, pp. 1254-1259.
 J. L. Prince and C. Xu, “A New External Force Model for
Snakes,” Proceedings of the IEEE Computer Society Con-
ference on Computer Vision and Pattern Recognition,
1997, pp. 66-71.