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ent types of images. Among various image segmenta-
tion techniques, active contour model [2] has emerged
as a powerful tool for semi-automatic object segmen-
tation. The basic idea is to evolve a curve, subject
to constraints from a given image,for detecting inter-
esting objects in that image. It consists in the reso-
lution of systems of partial differential equations for
which interface propagation phenomenon has to be
described. The active contour models are often imple-
mented based on level set method ((Sethian, 1999)),
which is a powerful tool to capture deforming shape.
But it has the disadvantage of a heavy computation re-
quirement even using the narrowband evolution. The
fast marching method is proposed for monotonically
advanced fronts ((Sethian, 1999)), and is extremely
faster than level set evolution. Generally, there are
three key problems needed to be solved to implement
the curve evolution methods. The first one is the ini-
tialization of the seed points. The second one is the
formulation of the speed function. And the last one is
the determination of the stopping criterion.
The level set methods have been widely applied
in medical imagery (Suri et al., 2002) in different do-
mains : the brain (Xie et al., 2005), the bone (Morigi
et al., 2004), the vascular trees (Farag et al., 2004)
and so on... The most common way to initialize the
level set is the manual selection of a ROI which seems
to be relevant ((Xu et al., 2000), (Farag et al., 2004)
and (Xie et al., 2005)). Sometimes a simple mouse
click combined with a fast marching approach (Fan, )
is used. In this case the final contour determined by
the fast marching step is the initial front of the level
set. Those methods are semi-automated while we are
focused on the automated methods. (Morigi et al.,
2004) proposed an automated method but the imag-
ing system is not the same as the subject of our study.
Our work consists of detecting tumors from the
whole body image volume acquired by a PET/CT de-
vice. We have no a priori knowledge on the loca-
tion of the tumor zone to detect. A contour evolution
model using a level set method with an initialization
based on thresholding is proposed in this paper.
The paper is organized as the following. Firstly,
an overview of our study is described. Secondly, the
principle of the level set method and its implementa-
tion will be exposed. The different steps of our ap-
proach and the associated results are then presented.
Finally we will conclude and give some perspectives.
2 OVERVIEW OF THE STUDY
2.1 Segmentation Framework
Our aim is at detecting the tumorous areas in the body
and in the brain from PET images and MRI images.
Any a priori knowledge about locations of the tumors
are taken into account. As the PET images are usually
noisy and bad contrasted, the methods based on the
image intensity or gradient are not efficient in these
cases. The statistical methods cannot be neither used
efficiently due to the small size of the tumor : they
are too small to get statistical properties comparing
with all images. The solution of the evolving contours
is interesting in this case because they can grow to
the expected size of the tumorous areas with help of
geometrical and intrinsic properties.
For segmenting the 3D images, we complete it
through a 2D slice-by-slice process. The proposed
framework consists of 3 steps: seed detection giving
a set of seeds which are susceptible belonging to the
tumor; seed selection allowing to obtain one seed con-
sidered as the initial tumor contour; contour evolution
according to an active contour model.
The seed detection consists of finding ROIs us-
ing intensity information. The areas of high glucose
activity lead to high gray levels observed in PET im-
ages. A thresholding of images can be carried out to
obtain the ROIs. The problem is how to choose the
threshold. As known, the histogram can give the in-
formation about the distribution of grey levels. The
maximum of the histogram is firstly found, which rep-
resents body tissus. Supposing that the number of pix-
els belonging to tumor regions has less than that of the
pique of histogram. The threshold is then defined as
the gray level on which the number of pixels equals to
the maximum multiplied by a proportionality factor α
which is given by experiences.
After the thresholding of images, several seeds are
obtained in which some of them do not belong to the
tumor. The big regions representing some anatomi-
cal regions which give high intensity, and very small
regions due to noise, are detected as seeds. The big
regions can be easily moved out from the seeds. A di-
latation, morphological mathematics operator, is car-
ried out to eliminate the small seeds. This seed se-
lection step allows us to delete aberrant seeds and to
keep that of tumorous areas. From the obtained initial
contours (seeds), a level set method is used to grow
them to find the tumor contours. In the next section,
this method is presented in details.
AUTOMATED TUMOR SEGMENTATION USING LEVEL SET METHODS
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