Sensors & Transducers, Vol. 180, Issue 10, October 2014, pp. 85-88
Sensors & Transducers
© 2014 by IFSA Publishing, S. L.
http://www.sensorsportal.com
White Blood Cell Nucleus Segmentation
Based on Canny Level Set
1
1
3
Qiu Wenhua, 2 Wang Liang, 3 Qiu Zhenzhen
Guangdong Mechanical & Electrical College, Guangzhou, 510515, China
2
Guangdong post, 510515, China
Zengcheng College of South China Normal University, Guangzhou, 510515, China
1
Tel.: 13560248550
E-mail: [email protected]
Received: 30 May 2014 /Accepted: 30 September 2014 /Published: 31 October 2014
Abstract: Cell image segmentation is an important study direction and white blood cell composition reveals
important diagnostic information about the patients. Manually counting of white blood cells is a tiresome, timeconsuming and susceptible to error procedure. Due to the tedious nature of this process, an automatic system is
preferable. In this automatic process, segmentation of white blood cells is one of the most important stages. In
order to solve problems of the traditional method for cell segmentation, a method of level-set 3D segmentation
for White blood cells was proposed using canny. The Level-set segmentation was based on geometric active
contour models instead of parameter active contour models. The method overcame the obscurity of white blood
cells' boundary by taking advantage of the structure of conforming anatomic arrange of threshold. Further, the
initial segmented results preprocessed were applied using anisotropic diffusion and the real border of cell was
detected using canny. Finally, the cell was finely segmented using the level-set method. Thus, the segmentation
of white blood cells could be done more accurately. Copyright © 2014 IFSA Publishing, S. L.
Keywords: White blood cell, Nucleus segmentation, Level-set, Canny.
1. Introduction
White blood cell nucleus segmentation is a key
technology in medical image processing, its task is to
extract interesting objects from the medical image in
order to serve the clinician computer-aided diagnosis.
Substituting automatic detection of white blood cells
for manually locating, identifying and counting
different classes of cells is an important topic in the
domain of cancer diagnosis. Automatic recognition
of white blood cells in hematological can be divided
into four major parts: preprocessing, image
segmentation, feature extraction and classification.
Segmentation of white blood cells is one of the most
important stages in this process [1].
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At present, many researchers study the variation
level set method applied in medical image
segmentation. Variation level set method widely used
in tracking and modeling, and it flourishes in the past
decade research both in theoretical and practical.
From the references we can see that the variation
level set methods become very popular in image
processing methods, therefore, the variation level set
is well suited for complex topologies strong noise
and low contrast areas of medical image analysis. In
this paper, we introduce a novel method of level-set
3D segmentation for white blood cells using canny.
The Level-set segmentation was based on geometric
active contour models instead of parameter active
contour models. The method overcame the obscurity
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Sensors & Transducers, Vol. 180, Issue 10, October 2014, pp. 85-88
of white blood cells' boundary by taking advantage of
the structure of conforming anatomic arrange of
threshold [2]. Further, the initial segmented results
preprocessed were applied using anisotropic
diffusion and the real border of cell was detected
using canny. Finally, the cell was finely segmented
using the level-set method. Thus, the segmentation of
white blood cells could be done more accurately.
great significance for numerical computation,
primarily because topological changes such as
breaking and merging are well defined and
performed “without emotional involvement”.
The motion is analyzed by connecting the φ

values (levels) with the velocity field v . This
elementary equation is
∂ϕ 
+ v • ∇ϕ = 0
∂t
2. Level Set Method
The level set method was first proposed by Osher
and Sethian (1988). It describes the propagating
fronts by a PDE (Malladi et al., 1995; Museth et al.,
2002), and it can solve the topological change of the
interface robustly. Some numerical methods
proposed to accelerate the solution (Sethian, 1999),
such as narrow band method, fast marching method,
higher order difference schemes. Now, the level set
method has become popular in many disciplines,
such as image processing, computer graphics,
computational
geometry,
optimization,
and
computational fluid dynamics.
The level set method is a numerical technique for
tracking interfaces and shapes. The advantage of the
level set method is that one can perform numerical
computations involving curves and surfaces on a
fixed Cartesian grid without having to parameterize
these objects (this is called the Eulerian approach).
Also, the level set method makes very easy to follow
shapes which change topology, for example when a
shape splits in two, develops holes, or the reverse of
these operations. All these make the level set method
a great tool for modeling time-varying objects, like
inflation of an airbag, or a drop of oil floating
in water.
The original idea behind the level set method was
a simple one. Given an interface Г in Rn of
codimension one, bounding an (perhaps multiply
connected) open region Ω, we wish to analyze and
compute its subsequent motion under a velocity field

Here v is the desired velocity on the interface,
and is arbitrary elsewhere. Actually, only the normal

component of v is needed ： vN = v •
x = x( x1 ,......, xn ) ∈ R n . The level set function ϕ has
the following properties [3]:
ϕ ( x, t ) > 0
ϕ ( x, t ) < 0
ϕ ( x, t ) = 0
for x ∈ Ω
for x ∉ Ω
∂ϕ
+ vN • | ∇ϕ |= 0
∂t
(3)
Here we give simple and computationally fast
prescriptions for reinitializing the function φ to be
signed distance to Г, at least near the boundary [4],
smoothly extending the velocity field vN off of the
front Г [5] and solving equation (3) only locally near
the interface Г, thus lowering the complexity of this
calculation by an order of magnitude [6]. This makes
the cost of level set methods competitive with
boundary integral methods, in cases when the latter
are applicable, e.g. see [7]. We emphasize that all this
is easy to implement in the presence of boundary
singularities, topological changes, and in 2 or 3
dimensions. Moreover, in the case which vN is a
function of the direction of the unit normal (as in
kinetic crystal growth [8], and Uniform Density
Island Dynamics [9-10]) then equation (3) becomes
the first order Hamilton-Jacobi equation

∂ϕ
+ | ∇ϕ | γ ( N ) = 0 ,
∂t
(4)

γ = γ (N )
where
 ∇ϕ
N=
| ∇ϕ | .
a given function of the normal,
High order accurate, essentially non-oscillatory
discretizations to general Hamilton-Jacobi equations
including (4) were obtained in [11], see also [12]
and [13].
(1)
for x ∈ ∂Ω = Γ(t )
Thus, the interface is to be captured for all later
time, by merely locating the set Г(t) for which φ
vanishes. This deceptively trivial statement is of
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∇ϕ
,
| ∇ϕ |
so (2) becomes

v . This velocity can depend on position, time, the
geometry of the interface (e.g. its normal or its mean
curvature) and the external physics. The idea, as
devised in 1987 by S. Osher and J. A. Sethianis
merely to define a smooth (at least Lipschitz
continuous) function ϕ ( x , t ) , that represents the
interface as the set where ϕ ( x, t ) = 0 . Here
(2)
3. Edge-detection Method Based
on the Canny Operator
Canny brought out the rule that excellent edgedetection method should pleased in 1986 [14]:
Sensors & Transducers, Vol. 180, Issue 10, October 2014, pp. 85-88
1) Fine SNR (Signal-to-Noise), is that the
probability of the edge-point to be mistaked
must be low;
2) Good performance of positioning, is that the
detected edge-point should be at the center of real
edge to its best possibility;
3) Single edge response, is that the single edge
has only one response, and the false edge should be
restrained mostly.
The course of edge-detection by canny operator
includes: the first step, noise reduction by Gaussian
smoothing function, for the late process to prepare;
the second step, first-order difference convolution
template is used to calculate ladder degree of
amplitude and direction; the third step, for the
maximum inhibition of gradient amplitude.
segmentation method is carried out on a Pentium IV,
CPU 2.4 GHz, EMS memory 1G computer.
Fig. 2 (a, c, e) are three original white blood cell
images. We could get the cell approximate contour as
the level set method initial evolvement curve. During
the evolvement process, the curve closes upon the
cell contour step by step. Further, using canny to
detect the real border of cell. Finally, the cell was
finely segmented using the level-set method. So we
can pick up the cell contour accurately as shown
respectively in Fig. 2 (b, d, f). The experimental
results indicate that the algorithm can pick up the
object contour exactly and effectively.
4. The Proposed Algorithm
As mentioned above, the canny can detect the
approximate contour quickly, and the level set
method can pick up the edge exactly. This paper
takes advantage of both the canny and level set
method. First, using level-set method to overcome
the obscurity of white blood cells' boundary by
taking advantage of the structure of conforming
anatomic arrange of threshold. Further, the initial
segmented results preprocessed were applied using
anisotropic diffusion and the real border of cell was
detected using canny. Finally, the cell was finely
segmented using the level-set method. The method
would reduce the iteration times effectively. At the
same time, it can make level set method avoid across
boundary or astringing local. The availability of the
new algorithm is well proved in the cell
image detection. The algorithm flow chart is shown
in Fig. 1.
(a) Original image
(b) The cell nucleus contour
picked up
(c) Original image
(d) The cell nucleus contour
picked up
(e) Original image
(f) The cell nucleus contour
picked up
Fig. 2 White blood cell images.
6. Conclusions
Fig. 1. Algorithm flow chart.
5. Experiment Result
The white blood cell detection is very important
in the medical image processing. The proposed
This paper puts forward a segmentation algorithm
which combines canny with level set method. Its
main characteristic is that making use of level-set to
get the object approximate contour. It as the level set
initial evolvement curve can avoid striding over
boundary or constringency local. Weighting the edge
detection function of the level set function equation
can improve picking up weak edge object veracity
and reduce the level set method iterations. The new
algorithm is effective in the experiment of picking up
cell nucleus image contour.
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Sensors & Transducers, Vol. 180, Issue 10, October 2014, pp. 85-88
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