330
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,
VOL. 25,
NO. 3,
MARCH 2003
Watersnakes: Energy-Driven
Watershed Segmentation
Hieu Tat Nguyen, Marcel Worring, and Rein van den Boomgaard
Abstract—The watershed algorithm from mathematical morphology is powerful for segmentation. However, it does not allow
incorporation of a priori information as segmentation methods that are based on energy minimization. In particular, there is no control of
the smoothness of the segmentation result. In this paper, we show how to represent watershed segmentation as an energy minimization
problem using the distance-based definition of the watershed line. A priori considerations about smoothness can then be imposed by
adding the contour length to the energy function. This leads to a new segmentation method called watersnakes, integrating the strengths
of watershed segmentation and energy based segmentation. Experimental results show that, when the original watershed segmentation
has noisy boundaries or wrong limbs attached to the object of interest, the proposed method overcomes those drawbacks and yields a
better segmentation.
Index Terms—Watershed segmentation, energy-based segmentation, topographical distance, snakes.
æ
1
S
INTRODUCTION
EGMENTATION is a fundamental problem in image analysis.
It should yield a partitioning of the image into disjoint
regions, uniform according to some feature such as gray
value, color, or texture. The segmentation process can rely
both on the uniformity of the feature within the regions or on
edge evidence. In both cases, the result should be a balance
between adherence to the possibly noisy and incomplete data
and smooth segmentation results suited for further analysis.
There are two major approaches in segmentation: energybased and watershed-based. In the first approach, the
segmentation is obtained as result of the minimization of a
so-called energy function. The second approach is mainly
based on the watershed algorithm from mathematical
morphology. This paper investigates the relation between
these approaches.
A large number of segmentation methods in literature use
the first approach [1], [2], [3], [4], [5]. To balance data
adherence and smoothness, the energy is composed of a
data-driven term and a regularization term. The purpose of
the second term is to impose a priori knowledge, usually
smoothness of the region contour, on the segmentation result.
The data-driven terms used in literature can be classified
into two classes: contour-based (or snake-based) and
region-based.
Snake-based methods, as originally defined by Kass and
Witkin [4], use gradient information as input data. A contour,
balancing maximal edge evidence with minimal curvature
and curve length is found. The contour representation used in
this method, may encounter problems with topological
changes, like self-intersections [6], during optimization. To
avoid these problems, Osher and Sethian proposed a level set
approach [6]. The contour is represented as a level curve of a
. The authors are with the Intelligent Sensory Information Systems Group,
University of Amsterdam, Faculty of Science, Kruislaan 403, NL-1098 SJ,
Amsterdam, The Netherlands. E-mail: {tat, worring, rein}@science.uva.nl.
Manuscript received 15 Nov. 2000; revised 8 Sept. 2001; accepted 14 Aug.
2002.
Recommended for acceptance by L. Vincent.
For information on obtaining reprints of this article, please send e-mail to:
tpami@computer.org, and reference IEEECS Log Number 113145.
3D surface or boundary of a growing region. As the
representation is intrinsic, topological changes can be
handled. The level set method has also been used in the
geodesic model of Caselles et al. [5] and the bubble model of
Tek and Kimia [7]. Other common issues that need to be
addressed in the snake-based methods are: the decision when
to stop moving the contour, overcoming gaps between broken
edges, and the selection of the initial contours. The geodesic
model deals well with gaps, while the bubble model offers a
solution for initial contour selection by randomly initializing
a large number of seeds that grow and merge afterwards. In
both models, the criterion to stop contour motion, however,
depends on a predefined parameter. As a consequence, the
contour may converge to nonsignificant edges.
The region-based methods use a global energy function,
computed for the entire area of the regions, rather than their
boundaries only. The earliest models are the Bayesian
segmentation of Geman and Geman [8] and the one proposed
in Mumford and Shah [1]. In [3], Leclerc suggests a
segmentation approach based on the minimum description
length criterion. In [2], Zhu and Yuille considered this
criterion in the continuous domain and proposed a segmentation method named region competition. In all existing
region-based methods, the uniformity in a region is described
using a parameterized probability distribution of intensity.
The energy yields a trade-off between how well the models
describe the region and the smoothness of the contours. Since
parameters of the distributions are unknown, the algorithm
needs to go back and forth between the determination of the
region parameters and the determination of the region labels.
The methods, therefore, are complex and computationally
expensive.
Apparently, the watershed algorithm from mathematical
morphology takes a very different approach, compared to
the energy-based methods described. The input is a relief
function representing edge evidence, where the morphological gradient is a common choice for computing such a
relief. By viewing this function as a mountain landscape,
object boundaries are determined as watershed lines.
In [9], [10], the watershed algorithm is implemented via
region growing, where seeds are the regional minima of the
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NGUYEN ET AL.: WATERSNAKES: ENERGY-DRIVEN WATERSHED SEGMENTATION
relief. To prevent oversegmentation due to a possibly large
number of minima in the original edge evidence function, the
watershed algorithm from selected markers is usually
employed [9], [11]. This technique selects markers from
significant minima and modifies the relief accordingly such
that nonsignificant minima are filled up. The markers are
usually extracted as low gradient zones in the image,
smoothed by a morphological filter such as opening by
reconstruction [12].
The watershed algorithm has been proven powerful for
contour detection [13], [14] as well as image segmentation
[9], [11]. Compared to the other methods mentioned, the
watershed has several advantages, including the proper
handling of gaps and the placement of boundaries at the
most significant edges (see Section 4).
It is difficult, however, to impose a priori information, in
particular smoothness, on the watershed lines. As the
algorithm relies purely on the edge evidence, the boundaries
between regions may be noisy. A simple postprocessing
smoothing of the watershed line does not take into account
the observed information and, therefore, important corners
with high edge evidence may be lost. So, the question is
whether we can represent the watershed segmentation as the
result of the minimization of an energy function. If so, a priori
information can be imposed by adding appropriate regularization terms to the energy. This is achieved in this paper,
leading to a new method called watersnakes.
The paper is organized as follows: Section 2 provides the
necessary background on watershed segmentation. In
Section 3, we derive the cost function, whose minimization
is equivalent to the watershed segmentation. We then add
the contour length to this function to produce a segmentation with smooth boundaries. Section 4 derives properties of
watersnakes, including a detailed comparison with the
above energy-based methods. Section 5 develops algorithms for the implementation. Finally, Section 6 shows
experimental results.
2
DEFINITION AND PROPERTIES OF
THE WATERSHED
Several distance-based definitions of the watershed have
been proposed by Meyer in [15], Najman and Schmitt in
[13], and Preteux in [16]. Although the definitions are
slightly different, all papers show that, with an appropriate
measure of the distance traveled over the relief, the
watershed line is the set of points equidistant to the
regional minima of the relief function. The definitions are
valid for both the continuous and the discrete case. In this
section, we briefly consider the definitions and results
needed for this paper.
2.1 The Topographical Distance
The heart of the distance-based definition of the watershed
is the concept of topographical distance.
Suppose the watershed is defined for a given relief
function fðxÞ : X !
7 IR on some domain X IR2 .
Definition 1. For any smooth function f, the topographical
distance between two points x and y is a spatial distance
weighted with the gradient norm jrfj:
331
Lðx; yÞ ¼
inf
>
e y
2½x
Z
jrfð ðsÞÞjds;
ð1Þ
where ½xe>y denotes the set of all possible paths from x to y.
As indicated, the above definition is valid for smooth
functions only. When the function is nonsmooth, the topographical distance is defined in a more complicated way [15].
For the case where f is defined on a digital grid: X ZZ2 , a
discretized version of (1) has been proposed by Meyer in [15]:
L~ðx; yÞ ¼ min
n
X
rðtiÿ1 ; ti Þ:distðtiÿ1 ; ti Þ;
ð2Þ
i¼2
where distðÞ denotes the Chamfer distance [17]. The minimum is taken over all possible paths of adjacent pixels ¼
t1 ; t2 ; . . . ; tn from x to y, where t1 ¼ x, tn ¼ y, and ti ; tiþ1 are in
the same Chamfer neighborhood. Here, rðtiÿ1 ; ti Þ is an
approximation of the gradient norm. Based on the work by
Meyer in [15], we propose to compute rðtiÿ1 ; ti Þ as follows:
8
if fðtiÿ1 Þ < fðti Þ
< LSðti Þ
if fðtiÿ1 Þ > fðti Þ ð3Þ
rðtiÿ1 ; ti Þ ¼ LSðtiÿ1 Þ
:
minfLSðti Þ; LSðtiÿ1 Þg if fðtiÿ1 Þ ¼ fðti Þ;
where LSðxÞ is the lower slope at x [15]. The definition is
identical to the one of Meyer in [15] except when
fðtiÿ1 Þ ¼ fðti Þ. As we have shown in [18], (3) is advantageous over the definition in the reference in cases where the
latter leads to the shifting of the watershed line.
2.2 The Watershed Line
Now, suppose the function f has a finite number of distinct
regional minima [9], denoted M1 ; . . . ; MK . Let i be the level
of f on Mi . For each regional minimum, we consider the
corresponding topographical distance transform:
Li ðxÞ ¼ Lðx; Mi Þ ¼ inf Lðx; yÞ:
y 2 Mi
ð4Þ
For each point, K values of distance to the regional minima
are obtained. The catchment basins and the watershed line
are then defined as:
Definition 2. The catchment basin CBi of a regional minimum
Mi is defined by:
CBi ¼ fx 2 X j 8j 6¼ i; 1 j K :
i þ Li ðxÞ < j þ Lj ðxÞg:
ð5Þ
Definition 3. The watershed line of the function f is the set of
points not belonging to any catchment basin:
[
ð6Þ
W SðfÞ ¼ X n CBðMi Þ:
i
In case all regional minima are at the same level, the
watershed is the topographical SKIZ (skeleton by zones of
influence [19]) of the regional minima.
It is important to note that the function f can be fully
reconstructed from the distances to the regional minima as
follows: [20], chapter 5], [21]:
fðxÞ ¼ min f
1iK
i
þ Li ðxÞg:
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ð7Þ
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,
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NO. 3,
MARCH 2003
In [16], CðÞ is called the connection cost of . Here, we
show that it can be used to define the reconstructed relief.
Proposition 1. g1 ðxÞ can be interpreted as the maximal level the
water from the markers has to reach before flooding x:
g1 ðxÞ ¼
ð10Þ
min CðÞ;
>
e Sm
2½x
where ½x e>Sm denotes the set of paths, thus linking x and Sm .
Fig. 1. Illustration of the watershed line and topographical distance in the
one-dimensional case.
As illustrated in Fig. 1, i þ Li ðxÞ equals fðxÞ within the
catchment basin CBi . When the pixel moves beyond the
catchment basin, for the 1D case i þ Li ðxÞ is the reflection
of fðxÞ with respect to the horizontal line passing through
the watershed point. For the 2D case, a similar behavior of
i þ Li ðxÞ can be observed, except that the reflection is far
more complicated as the watershed line has varying height.
The watershed line may be thick, i.e., have a nonzero area.
It may also have so-called barbs which are branches of zero
area with an end point [13]. Several approaches have been
proposed to define a thin watershed line [11], [16], [22]. These
methods add the geodesic distance to the topographical
distance. This makes the definition much more complicated.
In addition, we notice that the geodesic SKIZ itself may also be
thick (see Appendix A). As a consequence, the referenced
approaches yield a thinner watershed line, compared to the
one in Definition 3, but they cannot guarantee a watershed
line of zero area.
2.3 The Watershed from Selected Minima
In practice, to prevent oversegmentation, the watershed line
is constructed from a given set of regions called markers. A
modification of the relief function is required [11]. First, all
points in the markers are given a lowest value, say, 0. A new
relief is constructed by the recursive conditional erosion:
gnþ1 ¼ maxff; "gn g;
ð8Þ
where g0 ðxÞ is the function, which has the value 0 on the
markers and 1 otherwise, and "gn denotes the erosion of gn
with a minimum disk. The watershed transformation is then
applied to g1 which has the markers as its sole minima. The
classical watershed line of g1 is a subset of the watershed
line of the original function f with the most significant edges
retained.
We add another interpretation of g1 that has a generic
property with respect to (8). Let Sm be the set of markers.
Given a path from x to Sm . Let
CðÞ ¼ max fðzÞ:
z2
ð9Þ
The proof is given in [18].
Another point of view on g1 , which also does not require
recursion, has recently been given by Meyer in [23].
We have described in this section the distance-based
definition of the watershed line. We have modified the
definition of the topographical distance in a discrete grid [15].
The modified definition guarantees the placement of the
watershed line at ridgelines of the relief and the possibility to
recover the relief from the topographical distance to regional
minima. We also give a new and more generic definition for
the reconstructed relief in the watershed algorithm from
markers. This new definition does not require the recursive
erosion as in the traditional approach [11]. The definition of
the watershed, based on the topographical distance, is needed
in the remaining part of the paper for the investigation of the
relation between the watershed and the energy minimization.
3
WATERSHED
AS A
MINIMIZATION PROBLEM
In this section, we establish the relation between watershed
and energy-based minimization.
3.1 The Energy Function
Unless stated otherwise, the notations in this subsection are
used for both the continuous and the discrete case.
Let us first define segmentation as a partition of the
image space:
Definition 4. A partition of the image space X is a set of
connected regions 1 ; . . . ; K such that
aÞ
K
[
i
¼ X;
i¼1
bÞ 8i 6¼ j :
cÞ Intð
iÞ
i
¼
\
j
¼ ;;
ð11Þ
i;
where IntðRÞ denotes the interior of set R and R its closure.
The last condition is meaningful only in the continuous
case. It ensures that the boundaries of the regions do not
have barbs. This is equivalent to saying that each boundary
segment must separate at least two different regions.
Definition 5. A partition
segmentation if
8x 2
i
:
i
1; . . . ;
K
þ Li ðxÞ ¼ min f
1jK
is called a watershed
j
þ Lj ðxÞg:
ð12Þ
Considering (7), the right-hand side of (12) actually
equals fðxÞ.
It follows from Definition 5 that each region of a watershed
segmentation contains one and only one catchment basin and
the borders of the regions lie within the possibly thick
watershed line (see Fig. 2). Note that the inverse statement is
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NGUYEN ET AL.: WATERSNAKES: ENERGY-DRIVEN WATERSHED SEGMENTATION
333
In conclusion, Theorem 2 states the equivalence of the
watershed to energy minimization.
Fig. 2. (a) Illustration of a watershed segmentation. (b) Illustration of a
partition that is not a watershed segmentation.
true only in case of a thin watershed line. When the watershed
is thick, one can find a partition for which the region
boundaries lie in the watershed region but (12) does not hold.
An important question is whether a watershed segmentation actually exists. The existence is obvious in case of thin
watershed lines. However, when the watershed region is
thick, it is not clear how to assign pixels in the watershed
region to the catchment basins such that Definitions 4 and 5
are satisfied. To this end, we have proven the following.
Proposition 2. Every catchment basin CBi is a connected set.
See [18] for a proof. This proposition is then needed for the
proof of the following theorem.
Theorem 1. For a smooth function f, defined on an open,
bounded, and connected domain X , there always exists at least
one watershed segmentation.
Proof. See Appendix A. The main idea is to assign all pixels
in the watershed region to one catchment basin unless
(12) or the requirements of a partition are violated.
u
t
Although we prefer to accept the fact that the watershed
line can be thick, the proof of Theorem 1 actually specifies a
way to construct a watershed line, which has zero area, no
barbs, and at the same time, satisfies (12). As noted in the
previous section, none of the existing approaches can
guarantee a watershed segmentation with an ideal thin
watershed line.
Given Theorem 1, we can now take a next step by
proving:
1; . . . ;
Theorem 2. A partition
function:
Eð
1; . . . ;
KÞ
¼
K ZZ
X
i¼1
K
f
minimizes the following
i
þ Li ðxÞgdx
ð13Þ
i
if and only if it is a watershed segmentation.
The proof is given in Appendix B.
Looking at (13), we see that in case i ¼ 0, EðÞ is actually
the sum of the volumes of the topographical distance function
over the regions. We remark that, in fact, SKIZ with respect to
any distance measure can be obtained by minimizing a cost
function similar to (13). In our case, the minimization of E
yields the SKIZ with respect to topographical distance, which
is the watershed line. We call the function E energy as this is
the common term for a cost function whose minimization
yields a desired segmentation.
3.2 Watershed and PDEs—Imposing Smoothness
A PDE form of the watershed line can be obtained by
considering the minimization of the energy E in (13) along
the steepest descent direction.
RR
f iþ
The differentiation of the area functional
i
Li ðxÞgdx with respect to a deformation of the border @ i
@
of i results in the equation @x
ni , where x@
@t ¼ f i þ Li ðx@ Þg~
is a point on @ i and ~
ni is the unit normal vector of @ i
pointing inwards into i [2]. Thus, the value i þ Li plays
the role of a “force” compressing the region i from its
boundary. A boundary point adjacent to two regions i and
ni and ð j þ Lj Þ~
nj . Since
j is under two forces ð i þ Li Þ~
~
ni , the motion is given by
nj ¼ ÿ~
@x@
¼ fð
@t
i
ÿ
jÞ
þ ðLi ÿ Lj Þg~
ni :
ð14Þ
Thus, x@ moves as long as it still resides inside one of the
6 0 and stops
catchment basins where ð i ÿ j Þ þ ðLi ÿ Lj Þ ¼
when this value equals zero. In the end, the boundary
resides in the watershed line.
Having represented the watershed segmentation as an
energy minimization, the smoothness of the region boundary is now achieved by adding the boundary length to the
energy function
0
1
Z
ZZ
K
X
B
C
dsA; ð15Þ
f i þ Li ðxÞgdx þ
Eð 1 ; . . . ; K Þ ¼
@
i¼1
@
i
i
where is the weighting coefficient.
We call the segmentation method based on the minimization of this energy function watersnake in order to
distinguish it from the original watershed. In this case, the
compressing force acting on a point x@ at the border of a
ni , where i denotes the
region i is ð i þ Li þ i Þ~
curvature of @ i at x@ . When two regions i and j are
neighbors, we have i ¼ ÿj ¼ on their common boundary. The motion of the boundary is then caused by the
vector sum of the two compressing forces
@x@
¼ ð
@t
i
ÿ
jÞ
þ ðLi ÿ Lj Þ þ 2 ~
ni :
ð16Þ
Some other PDE-based formulations for the watershed
line have been used in literature. In [7], Tek and Kimia
notice that the watershed algorithm on a relief function f
1~
@
can be implemented by the evolution equation @x
@t ¼ f n.
In [24], Maragos and Butt give another equation:
@x@
1 ~
@t ¼ jrfj n. Both these equations describe the evolution
of marker contours before the markers meet, i.e., when
the contours reside within the corresponding catchment
basins only. Our PDE (14), in contrast, describes the
motion of an arbitrary contour toward the watershed line.
The formulation in [7] and [24] can be used for
computing the original watershed line. However, in case
the evolution equation also contains the regularization
term, these approaches are not appropriate.
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VOL. 25,
NO. 3,
MARCH 2003
Fig. 3. Classification of segmentation methods.
4
COMPARISON OF WATERSNAKES WITH
ENERGY-BASED SEGMENTATION METHODS
In this section, we compare watersnakes with other energybased segmentation methods.
It is clear from (15) that the energy function of watersnakes belong to the region-based group (see Fig. 3). In
particular, it can be compared to the energy used by Zhu
and Yuille in [2]. The latter energy is defined as follows:
1
0
Z
Z
Z
K
XB
C
ð17Þ
dsA;
ÿ log P ðIx jai Þdx þ
min
@
i¼1
i
@
i
where P ðIx jai Þ denotes the probability density of the
measured intensity at pixel x under the assumption that
the pixel belongs to the region i and ai denotes the
parameter vector of this distribution.
It can be observed that the energy function of watersnake in
(15) and the energy function of the region competition method
in (17) are similar except that the topographical distance i þ
Li ðxÞ in (15) replaces the term ÿ log P ðIx jai Þ in (17). Both these
terms measure the homogeneity within region i but the
topographical distance has the major advantage that it does
not contain any parameters. The watersnake does not have to
go back and forth between updating region parameters and
updating pixel’s labels. The minimization procedure is
therefore simpler than those in the region competition and
other methods using region-based energy.
The watersnake model has also advantageous properties
over the snake-based methods:
1.
While the snake-based methods [4], [5], [7] might
converge to weak edges, depending on the tuning of
parameters, the watershed line always corresponds to
the most significant edges between the markers.
When the smoothing term is present, the watersnake
deviates from the watershed line to reduce the local
curvature until ð i ÿ j Þ þ ðLi ÿ Lj Þ is compensated
with 2 . The smoothing effect is inversely proportional to the sharpness of edges since the value of
ð i ÿ j Þ þ ðLi ÿ Lj Þ increases faster for sharp edges
when the contour moves away from the watershed
line.
2. In case evidence for edges between the markers is low,
the resulting contours still lie between the markers.
The watersnake is therefore able to fill the gaps
between broken edges.
In conclusion, the watersnake is a fusion of energy-based
segmentation and morphology-based segmentation and
combines the advantages of these two approaches.
5
IMPLEMENTATION
There are two possible approaches for implementing the
minimization of the energy (15) in the discrete domain. The
first approach takes the motion (16) as its starting point.
Discretization of this equation requires a discrete estimator
of curvature. The second approach discretizes the energy in
(15) and defines a method for minimizing it. This approach
requires a discrete estimator of contour length.
These two approaches are investigated in detail in the
two sections that follow.
5.1 Algorithm Based on Region Growing
For the discretization of PDEs of contour motion depending
on curvature, the well-known level set method of Osher and
Sethian in [6] can be applied. This method is appropriate for
solving (16) in case of two markers. The method would,
however, grow expensive in case of multiple markers. We
have to keep track of every boundary between every pair of
potentially adjacent regions each with its own PDE. At the
same time, we would have to take care that the topology of the
entire partition is preserved.
A simpler approach is to modify the region growing step
of the original watershed algorithm such that the region
borders are kept smooth during growth. Starting from a set
of markers, the algorithm iteratively adds to the markers the
pixels connected to their border. For the pixel selection, the
growing process takes into account the value of the
compressing force i þ Li þ i at each boundary pixel.
The part of the border, where this force is weakest, is
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NGUYEN ET AL.: WATERSNAKES: ENERGY-DRIVEN WATERSHED SEGMENTATION
335
Although these coefficients are obtained with circles, they
also yield a good estimate for straight boundaries, as
indicated in Fig. 5. Note that the above contour length
estimator will not be accurate for small-size regions like a
single pixels since it is derived for disks of larger radius.
5.3 Algorithm Based on Energy Discretization
In this section, we propose an algorithm that aims to
minimize the following discrete version of the energy (15)
Fig. 4. For the indicated pixel: n4 ðxÞ ¼ 2; n8 ðxÞ ¼ 2; nk ðxÞ ¼ 5.
E~ð
propagated in the outward direction first. The curvature i
at a pixel is computed from the local boundary pixels [25].
Every time a pixel is added to the markers, the curvature at
the boundary pixels in the neighborhood is recomputed.
A similar idea has been proposed in the segmentation
methods of [26] and [27]. In [26], the criterion for adding a
pixel to a marker is based on both contrast and contour
complexity. The latter term is defined as the number of
contour points added to the marker contour when the pixel in
consideration is added to the marker. This approach is then
extended in [27] for region growing applied to flat zones.
Note that, although the intuitive approach of keeping
region contours smooth during growth yield smooth
resulting boundaries in many practical situations, it does
not guarantee to yield a solution of (16). As a consequence,
we do not consider this algorithm any further in the paper.
We now concentrate on (15).
5.2 Intermezzo: Local Contour Length Estimate
Before describing in the next section an algorithm minimizing the energy (15), we need a contour length estimator.
We estimate the contour length using the number of pairs of
neighboring pixels. In this way, the contour length can be
re-estimated locally when the contour moves. We use the
Chamfer neighborhood. Given pixel x 2 i , let n4 ðxÞ; n8 ðxÞ,
and nk ðxÞ be the number of pixels which are in the four,
eight, and “knight-move” connected neighborhood of x but
outside i (see Fig. 4).
The perimeter of i is then estimated as:
Z
X
½w4 n4 ðxÞ þ w8 n8 ðxÞ þ wk nk ðxÞ;
ð18Þ
ds ¼
@
x2
i
i
where w4 , w8 , and wk are the weighting coefficients.
The motivation of (18) is the observation that contour
length is proportional to the number of neighbor pairs
crossing the contour. As will be seen, the advantage of this
approach is that we can calculate how much region
perimeter increases when a pixel is added to the region
border by looking at local information only. To determine
the values of the weighting coefficients, we do an experiment
to find the relation between n4 , n8 , nk , and the theoretical
perimeter of circles and rectangles. The results are shown in
Fig. 5. The results for circles confirm the linear relationship
between the perimeter and n4 , n8 , and nk , namely,
2r 0:79n4 0:56n8 0:18nk , where r is the circle radius.
Taking the final length as the average of the three individual
estimates, we set
w4 ¼ 0:26;
w8 ¼ 0:19;
wk ¼ 0:06:
ð19Þ
1; . . . ;
KÞ
¼
K Xn
X
i¼1 x2
i
i
þ L~i ðxÞþ
o
½w4 n4 ðxÞ þ w8 n8 ðxÞ þ wk nk ðxÞ :
ð20Þ
The smoothing term used in (20) is somewhat similar to that
in the Bayesian model [3], [28]. This term is nonzero only
near the region boundary.
The algorithm needs a partition of the image as starting
point. For example, the original watershed segmentation
can be used. Then, an exchange of boundary pixels between
the regions is performed such that the energy (20) is
minimized. Here, we define boundary pixels as ones having
at least one neighbor with a different label.
Suppose a boundary pixel x is currently assigned to i and
it is adjacent to region j . Let n4 ðx; jÞ; n8 ðx; jÞ, and nk ðx; jÞ be
the number of pixels with label j, respectively, in the 4, 8, and
“knight-move”-connected neighborhood of x. If we change
the pixel assignment from label i to label j, n4 ðx; jÞ pairs of
4-connected pixels disappear, while n4 ðx; iÞ new pairs are
created. With the same observation for the other two kinds of
connectivity, it can be verified that the change of energy (20) is
E~ðx; i ! jÞ ¼ ð j ÿ i Þ þ ðL~j ðxÞ ÿ L~i ðxÞÞ
n
þ 2 w4 ½n4 ðx; iÞ ÿ n4 ðx; jÞ þ w8 ½n8 ðx; iÞ ÿ n8 ðx; jÞ ð21Þ
o
þ wk ½nk ðx; iÞ ÿ nk ðx; jÞ :
Relating this to (16), note that the last term in the right-hand
side of (21) can be regarded as a measure for the curvature
(but a inaccurate one).
Obviously, reassignment of x should occur if
E~ðx; i ! jÞ < 0. The value of E~ is called the stability
of x [28]. Junction points may have several reassignment
possibilities. In this case, the stability of a junction pixel
is defined as the minimal value of E~. For each
reassignment, the pixel with lowest stability is selected.
Since reassignments can only reduce the energy E~, the
convergence of the algorithm is guaranteed.
As noted, (21) uses a crude measure of curvature. This
measure takes into account only the local information at the
pixel considered and, therefore, smoothing occurs only at a
small scale. To smooth corner structures at larger scales, the
algorithm should run in multiscale mode. The minimization
result at a low-resolution level is used as the initial state for the
minimization at the next higher resolution level. One
advantage of multiscale smoothing is that resulting boundaries are not jagged as often happens in methods based on a
single scale [2]. In the latter approach, only global corners are
smoothed but local corners remain unaffected, causing
jaggedness.
Another advantage of the multiscale mode is the avoidance of undesired local minima of the energy function. As we
have proven, the original watershed segmentation gives the
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Fig. 5. Results of the experiment for the determination of w4 , w8 , and wk . (a) The numbers n4 , n8 , and nk are counted at the border of digital disks of
radius ranging from 5 to 200 and plotted as function of the radius. The plot shows a linear relationship between the numbers of neighbor pairs and the
perimeter of the disks: 2r 0:79n4 0:56n8 0:18nk . (b) The numbers n4 , n8 , and nk are counted at the border of a digital rectangle of size 210 194,
the orientation of which is then rotated with respect to the grid. The plots show the ratios (in percentage) of 0:79n4 , 0:56n8 , and 0:18nk , respectively, to
the theoretical perimeter of the rectangle as function of the rotation angle. (c) The same as in (b) but for the combination ð0:79n4 þ 0:56n8 Þ=2. (d) The
same as in (b) but for the combination of ð0:79n4 þ 0:56n8 þ 0:18nk Þ=3.
global minimum of the energy function (13). However, due to
adding the contour length as in (15) and (20), the energy
function may have many local minima. As the minimization
procedure finds a local minimum only, the result depends on
the choice of the initial segmentation. The coarse-to-fine
strategy prevents the algorithm from getting trapped in an
undesired local minimum by providing a good initial
segmentation, obtained at lower resolution levels.
The algorithm is summarized below: It consists of two
stages. The result of Stage 1 is identical to the original
watershed segmentation as in [15], [22]. Since Stage 1 increases
regions based on a minimum distance criterion, it is also
similar to the algorithm in [29] except for a different distance
measure. The major contribution of our algorithm, therefore,
is Stage 2 where the segmentation result of Stage 1 is smoothed
by minimizing energy (20).
ALGORITHM
STAGE 1
1. Compute K distance transforms Li ðxÞ ( see [15], [22] ).
2. Initialize the regions i from the regional minima Mi .
3. From unassigned pixels on the outer boundary of the
regions, select one with minimal value of i þ Li ðxÞ, where
i is the label of the adjacent region. Assign the selected pixel
to region i .
4. Iterate Step 3 until all pixels are assigned.
STAGE 2
1. Start from a low-resolution level by subsampling the
label image as well as the K distance transforms.
2. For every boundary pixel compute the stability according to (21).
3. Select the boundary pixel with lowest stability. When
this value is negative, perform the reassignment.
4. Recompute the stability of the pixels in the chamfer
neighborhood of the reassigned pixel according to (21).
5. Iterate Steps 3 and 4 until no reassignment is possible.
6. Repeat Steps 2-5 for higher resolution levels.
Let us give more details on the downscaling process in
Stage 2. The input for the minimization at the upper or
coarse resolution level l is obtained from the previous level
l ÿ 1 by subsampling the label image and the K distance
maps by a factor of two in both dimensions. To be specific, a
label of a pixel x at level l is selected from the labels of the
four corresponding pixels x1 , x2 , x3 , and x4 at the finer
resolution l ÿ 1 according the majority criterion. The
K topographical distance maps could be recomputed for
each level. However, since this recomputation is time
consuming, we use a simplified approach in which the
topographical distance Li ðxÞ at level l is obtained by
averaging of the values of Li ðx1 Þ, Li ðx2 Þ, Li ðx3 Þ, and
Li ðx4 Þ at level l ÿ 1.
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NGUYEN ET AL.: WATERSNAKES: ENERGY-DRIVEN WATERSHED SEGMENTATION
337
Fig. 6. (a) A brain image. (b) The relief computed from morphological gradient. (c) The markers extracted. (d) The result of the original watershed
segmentation, shown for comparison.
Since the final segmentation is obtained at the finest
resolution, the result is always a local minimum of the
energy (20). Note also that the algorithm is guaranteed to
converge since the energy function always decreases.
6
RESULTS
In this section, we show illustrations of the performance of
watersnakes in segmentation using the algorithms described.
Before the segmentation started, images were smoothed
by the opening by reconstruction filter [12]. For the
smoothed images, the morphological gradient rm ¼ I ÿ
"I was computed, where I and "I, respectively, denote the
gray-value dilation and erosion of I with the minimum disk.
For color images, we defined the gradient as the maximum of
the morphological gradients computed for the three
RGB channels. The markers were extracted as low-gradient
zones, where the gradient is below 0.5 times of the estimated
standard deviation of the gradient over the entire image.
Let
0
if x 2 markers
ð22Þ
hðxÞ ¼
rm ðxÞ otherwise
and
gðxÞ ¼
0
1
if x 2 markers
otherwise:
ð23Þ
The relief f was obtained by the reconstruction by erosion
of g with mask h. The reconstruction algorithm can be
found in [30].
Fig. 6a shows an original brain slice image of size
375 371. Fig. 6b shows the relief image obtained. The
markers extracted are shown in Fig. 6c. The result of the
original immersion-based watershed algorithm [10], [9] is
shown in Fig. 6d for comparison.
The segmentation results obtained with the energy
discretization-based algorithm, presented in Section 5.3, are
shown in Fig. 7 for different values of the smoothing
coefficient . The minimization was performed at three
scales. At the lowest resolution level, the image was reduced
by a factor of four in both dimensions. Although the
algorithm used a simple measure for curvature, in multiscale
mode, the smoothing result is quite satisfactory.
More segmentation results are shown in Figs. 8 and 9. To
focus the reader attention, in all examples except for the top
row of Fig. 8, we show the results for the object of interest only.
The result in the top row of Fig. 8 can be compared to
that in [2], which aimed to segment the same image. Our
segmentations have less regions. Moreover, the regions are
smooth and the most prominent regions in the image.
As observed in the results, the watersnake algorithm has
two advantages over the watershed. The first one is the ability
to impose smoothness on the segmentation result. In Figs. 6d
and 8a, the original watershed line is rather noisy and jagged.
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Fig. 7. The segmentation result of the watersnake algorithm based on energy discretization with (a) ¼ 10, (b) ¼ 50, (c) ¼ 100, and (d) ¼ 150.
Note, in comparison with the original watershed segmentation in Fig. 6d, that the results in figure are smoother, but still identify the main objects.
Fig. 8. Segmentation by (a) watershed and (b) watersnake ( ¼ 50). In the bottom row, the result is shown for the object of interest only.
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NGUYEN ET AL.: WATERSNAKES: ENERGY-DRIVEN WATERSHED SEGMENTATION
339
Fig. 9. Segmentation by (a) watershed and (b) watersnake ( ¼ 50). The results are shown for the object of interest only.
These contours are usually not what the user desires. Smooth
contours as in the results of Figs. 7 and 8b are important for
further investigation of object shape. The second important
advantage of the watersnake algorithm is that it prevents the
formation of wrong limbs, the unwanted regions attached to
the object of interest. These limbs often occur in the watershed
segmentation due to a leak at the object boundary. Examples
are shown in the bottom row of Fig. 8a and in Fig. 9a. As
observed in Figs. 8b and 9b, the watersnake algorithm has
significantly shortened the wrong limbs, resulting in much
better segmentation compared to the left column.
Apparently, smoothness of the resulting boundary is
always at the cost of losing adherence to edges at some
corners. Examples are some protrusions in Figs. 7c and 7d or
the foot of the cat in the top row of Fig. 9b, which have been
smoothed over. We still cannot give an optimal scheme for the
determination of the coefficient since this issue is subjective
and depending on the application. Nevertheless, the proposed method is advantageous over the traditional watershed and postsmoothing of the result. As noted in Section 4,
boundaries in our results are not smoothed equally but rather
depending on the sharpness of local edges, i.e., abruptness of
intensity change. Since important edges are usually sharp,
they tolerate the smoothing better than weak edges. This can
also be observed in Fig. 7. Pay attention to the indentation on
the top of the brightest region in the middle. Despite high
curvature, this indentation remains unaffected in Figs. 7a and
7b, while protrusions at other weaker edges have been
smoothed over. The intactness of the true limbs of the tree in
Fig. 8b is another example.
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Fig. 10. (a) An example of a thick geodesic SKIZ. The bold lines illustrate
the shortest geodesic paths from x to the seeds A1 and A2 . (b) A
partition that has a boundary of zero area.
7
CONCLUSION
This paper has established a connection between the wellknown watershed segmentation from mathematical morphology and energy-based segmentation methods. Using
the distance-based approach for the definition of the
watershed line, we have derived an energy function whose
minimization is equivalent to the watershed segmentation.
Using this point of view, a priori knowledge on boundary
smoothness or shape can now be taken into account. In
particular, we obtain smooth watersheds by adding the
length of the region boundary to the energy function. It
yields a new segmentation method called watersnake that is
implemented by a multiscale algorithm. Significant improvements in smoothness of segmentation results, using
the new method, have been confirmed by experiments.
Apart from the incorporation of a priori knowledge into
the watershed segmentation, the results of the paper can
also be used for the combination of the watershed lines
obtained on different relief functions. The minimization of a
linear combination of the energy functions yields a natural
trade-off in segmentation between different types of edge
indicating functions. In a forthcoming paper, we are
exploring this for tracking regions in an image sequence.
APPENDIX A
PROOF OF THEOREM 1
In this appendix, we prove the existence of at least one
watershed segmentation according to Definition 5.
When the watershed line is thick, the assignment of the
watershed pixels into the catchment basins is ambiguous.
This is known as the plateau problem. As noted, several
methods [10], [16], [11], [22] use the geodesic distance in
plateau to solve this ambiguity. Unlike the euclidean SKIZ
[19], the geodesic SKIZ may be thick (see Fig. 10). Therefore,
none of the above methods can guarantee an ideally thin
watershed line that has a zero area (or equivalently, empty
interior). In essence, the proof presented below specifies a
way to construct a watershed segmentation that has
boundaries of zero area.
Our solution to the plateau problem is to assign all pixels in
the plateau to one catchment basin unless the constraints of
connectedness and regularity of the partition are violated. If it
is desired that the watershed line lies in the middle of the
plateau, one can also use the geodesic distance to divide the
VOL. 25,
NO. 3,
MARCH 2003
Fig. 11. Illustration of the watershed region in case of three catchment
basins.
plateau first and then use the proposed approach to divide the
remaining geodesic SKIZ that may be thick.
We shall call a set A regular if IntðAÞ ¼ A. Such a set
does not have barbs although it may contain barbs of
other sets inside it. If all regions of a partition are regular
and, furthermore, the border of the union set X does not
have barbs, then the border of every region does not have
barbs either.
We use the following proposition that has been proven
in [18].
Proposition 3. Let S be an open set. Given K seeds: A1 ; . . . ; AK
which are disjoint subsets of S, where every Ai , i 2 f1::Kg, is
connected and regular. We assume further that every pixel in S is
connected to one of Ai .
Then, we can partition S into K disjoint regions R1 ; . . . ; RK
such that every region Ri , i 2 f1::Kg is connected and regular,
and, furthermore, Ai Ri .
Using this proposition, we now can prove Theorem 1.
Proof of Theorem 1. Given x 2 X , we use I ðxÞ to denote the
set of indices such that
8i 2 I ðxÞ :
i
þ Li ðxÞ ¼ min f
1jK
j
þ Lj ðxÞg:
ð24Þ
Let ðxÞ be the number of indices in I ðxÞ and
ð25Þ
S ðnÞ ¼ fx 2 X jðxÞ ng:
SK
ð1Þ
ðKÞ
¼ X . It can be
Note that S ¼ i¼1 CBi and S
verified that S ðnÞ is an open set and, furthermore, every
pixel in S ðnÞ is connected to one of the regional minima
(using a similar proof as in Proposition 2).
To illustrate why we need sets S ðnÞ , let us consider the
segmentation with three catchment basins, i.e., K ¼ 3.
The watershed, being the set of points with equal
distances to the two nearest catchment basins, is then
the union of the following sets (see Fig. 11):
W12 ¼ fx j
1
þ L1 ðxÞ ¼
2
þ L2 ðxÞ;
þ L1 ðxÞ < 3 þ L3 ðxÞg
¼ fx j 2 þ L2 ðxÞ ¼ 3 þ L3 ðxÞ;
2 þ L2 ðxÞ < 1 þ L1 ðxÞg
1
W23
W13 ¼ fx j
1
W123 ¼ fx j
1
þ L1 ðxÞ ¼ 3 þ L3 ðxÞ;
1 þ L1 ðxÞ < 2 þ L2 ðxÞg
þ L1 ðxÞ ¼
2
þ L2 ðxÞ ¼
3
þ L3 ðxÞg:
To complete the segmentation, the regions of the
catchment basins need to be extended to fill up the
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NGUYEN ET AL.: WATERSNAKES: ENERGY-DRIVEN WATERSHED SEGMENTATION
watershed region. Note, however, that points in W12
cannot be assigned to the catchment basin CB3 because
this would violate (12) in our watershed definition. For
that reason, we do not construct a partition of the whole
image immediately. Instead, we grow the catchment
basins within set S ð2Þ first. By our definition:
341
We consider the two following cases:
1.
S ð2Þ ¼ fxjðxÞ 2g
¼ CB1 [ CB2 [ CB3 [ W12 [ W23 [ W13 :
In this way, points in W12 will not be assigned to CB3
since W12 and CB3 are disconnected, as will be proven
below. The final segmentation is constructed by assigning points in the remaining region W123 to one of the
regions obtained from the partition of S ð2Þ .
We are now back to the main proof with a general K.
In order to prove the existence of a watershed segmentation, we construct a series of partitions of S ðnÞ
ðnÞ
ðnÞ
S ðnÞ ¼ R1 [ . . . [ RK ; n ¼ 1; . . . ; K
ð26Þ
APPENDIX B
PROOF OF THEOREM 2
ð27Þ
Proof. In this appendix, we prove the equivalence of the
watershed to the minimization of energy (13).
From (7), it follows that i þ Li ðxÞ fðxÞ. Therefore,
for any partition 1 ; . . . ; K we have:
with
ð1Þ
Ri
¼ CBi ; i ¼ 1; . . . ; K:
2.
There exists an index ‘ 2 I ðzm Þ and ‘ 62 I ðxÞ. This
implies ‘ þ L‘ ðzm Þ ¼ min0jK f j þ Lj ðxÞg. Since
this equality also holds at the limit z , we have
‘ 2 I ðz Þ. On the other hand, I ðxÞ I ðz Þ. It
follows that I ðxÞ [ f‘g I ðz Þ and, therefore,
ðz Þ, the number of indices in I ðz Þ is larger than
that in I ðxÞ: ðz Þ > n. This contradicts to the fact
that is contained in S ðnÞ .
I ðzm Þ is a subset of I ðxÞ and ðzm Þ < n. This implies
zm 2 S ðnÿ1Þ . Since i 62 I ðxÞ ) i 62 I ðzm Þ and, thereðnÿ1Þ
ðnÿ1Þ
ðnÞ
fore, zm 62 Ri
. It follows that zm 2 Rj
Rj
ðnÞ
for some j 6¼ i. At the same time, zm 2 Ri , this
ðnÞ
ðnÞ
contradicts to the fact that Ri \ Rj ¼ ;.
u
t
ðnÞ
ðnÞ
The regions R1 ; . . . ; RK are obtained by applying
ðnÿ1Þ
ðnÿ1Þ
; . . . ; RK as seeds. Then,
Proposition 3 for S ðnÞ with R1
ðKÞ
ðKÞ
the final regions fR1 ; . . . ; RK g constitute a disjoint
ðKÞ
partition of X , where each region Ri is connected and
Eð
KÞ ¼
1; . . . ;
i¼1
regular.
ðKÞ
It remains to prove that fRi g is a watershed
segmentation. We shall prove that for every n ¼ 1; . . . ; K,
8x 2
ðnÞ
Ri
:
i
þ Li ðxÞ ¼ min f
0jK
j
þ Lj ðxÞg:
ð28Þ
We employ the principle of mathematical induction. Since
ð1Þ
Ri ¼ CBi , (28) holds for n ¼ 1 by the definition of the
catchment basins. Assuming that (28) holds for n ÿ 1, we
shall prove that it also holds for n.
ðnÞ
Let x 2 Ri . We assume conversely that
i
þ Li ðxÞ > min f
1jK
j
þ Lj ðxÞg:
That also means i 62 I ðxÞ.
As assumed, (28) holds for n ÿ 1, it follows that
ðnÿ1Þ
ðnÞ
ðnÞ
. Furthermore, since 8j 6¼ i : Ri \ Rj ¼ ;
x 62 Ri
ðnÿ1Þ
ðnÞ
ðnÞ
ðnÿ1Þ
Rj , we also have Ri \ Rj
¼ ;. From
and Rj
ðnÞ
ðnÿ1Þ
x 2 Ri , it follows that x does not belong to any Rj
,
j 6¼ i. It follows that ðxÞ > n ÿ 1.
ðnÞ
At the same time, as assumed, x 2 Ri S ðnÞ and
from the definition of S ðnÞ , we have ðxÞ n. Combining
ðxÞ > n ÿ 1 and ðxÞ n, we conclude ðxÞ ¼ n.
ðnÞ
ðnÞ
Since Ri is connected, one can find a path in Ri
from x to CBi , where ð0Þ ¼ x and ð1Þ 2 CBi . Observe
that the sets I ð ð0ÞÞ and I ð ð1ÞÞ are different. Therefore, if we travel along from x to CBi , a change in
I ð ðtÞÞ occurs somewhere. Let z be the first point
where I ð ðtÞÞ changes. In formulas, z ¼ ðt Þ, where
t ¼ supft0 j80 t < t0 : I ð ðtÞÞ I ðxÞg. By this definition, one can find a sequence zm ¼ ðtm Þ, where tm > t
and limm!1 zm ¼ z such that I ðzm Þ 6 IðxÞ.
K ZZ
X
f
i
þ Li ðxÞgdx
i
K ZZ
X
i¼1
fðxÞdx ¼
i
ZZ
ð29Þ
fðxÞdx:
x2X
For a watershed segmentation, the function E is minimized
since (29) becomes an equality. On the other hand, if (29) is
an equality, then
ZZ
f i þ Li ðxÞ ÿ fðxÞgdx ¼ 0:
8i :
i
Since
i
þ Li ðxÞ ÿ fðxÞ 0, it follows that
ZZ
f i þ Li ðxÞ ÿ fðxÞgdx ¼ 0
U
for any set U i . That means: 8x 2 Int i : i þ Li ðxÞ ¼
fðxÞ and as both Li ðxÞ and fðxÞ are continuous, this
equality holds also on Intð i Þ ¼ i :
8x 2
This implies
i
1; . . . ;
:
i
K
þ Li ðxÞ ¼ fðxÞ:
ð30Þ
is a watershed segmentation. t
u
ACKNOWLEDGMENTS
The authors would like to thank Professor Arnold Smeulders
for his valuable comments on earlier versions of this paper.
They would also like to thank the anonymous reviewers for
their helpful comments and suggestions that have improved
the paper.
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Hieu Tat Nguyen received the Eng and MSc
degrees in computer technology from the University “Lvivska Polytechnica” of Lviv, Ukraine in
1994. He received the PhD degree in computer
science from the University of Amsterdam, the
Netherlands, in 2001. He is currently a postdoctoral fellow with the Intelligent Sensory
Information Systems group at the University of
Amsterdam. His current research interests
include image sequence analysis, object tracking, object recognition, active learning, mathematical morphology, and
content-based image retrieval.
Marcel Worring received the masters degree in
computer science (honors) from the Free University, Amsterdam. His PhD thesis was on
digital image analysis and was obtained from the
University of Amsterdam, The Netherlands, in
1993. He became an assistant professor in 1995
in the Intelligent Sensory Information Systems
group at the University of Amsterdam. His
current interests are in multimedia information
analysis, in particular, document and video
analysis. He has been a visiting researcher in the Department of
Diagnostic Imaging at Yale University (1992) and at the Visual
Computing Lab at the University of California, San Diego (1998).
Rein van den Boomgaard graduated from Delft
University in 1988 and received the PhD degree
from the University of Amsterdam in 1992. He is
an assistant professor in the Intelligent Sensory
Information Systems (ISIS) group at the University of Amsterdam. Currently, he is working in
the field of computer vision and image processing with research interests in color vision, scalespace theory, and mathematical morphology.
Since 1999, he has also been an independent
consultant for companies needing advice and feasibility studies in the
areas of image processing and computer vision.
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