AUTOMATED COMBINED TECHNIQUE FOR SEGMENTING

CYTOLOGICAL SPECIMEN IMAGES

D. M. Murashov

Computing Centre of the Russian Academy of Sciences, 40, Vavilov street, Moscow, GSP-1, 119991, Russia

Keywords: Segmentation technique, active contour, dynamic system, height ridges, wave equation, cytological images.

Abstract: Automated snake-based combined technique for segmenting cytological images is proposed. The main fea-

tures of the technique are: implementation of the wave propagation model and modified Gaussian filter

based on the heat equation with heat source, availability of coarse and precise levels of contour approxima-

tion, automated snake initiation. The technique is successfully implemented for segmenting cytological

specimen images.

1 INTRODUCTION

One of the problems arising in the field of automated

diagnostics of hematological diseases is the segmen-

tation of nuclei in the cytological specimen images

for subsequent calculation of diagnostic features. It

is necessary to obtain such a closed curve in the

specimen image that follows the boundary of select-

ed nucleus with an adequate accuracy.

Haralick and Shapiro (Haralick and Shapiro,

1985) established the following requirements to im-

ages leading to successful segmentation: homogene-

ousness of regions in image with respect to some

characteristics; topological simplicity, significant

difference of characteristics of adjacent regions;

simplicity, smoothness, and spatial accuracy of re-

gion boundaries.

An image of a lymphoid tissue stained by Ro-

manovski-Giemsa technique is a color image (24

bpp) taken by a camera mounted on Leica DMRB

microscope using PlanApo 100/1.3 objective. The

equivalent size of a pixel was 0,0036 µ

. Cytological

specimen images have the following specific fea-

tures plaguing the solution. Firstly, because of poor

dye quality the boundary between cytoplasm and a

cell nucleus may be indistinctive. Secondly, cells

may be located closely to each other, in part may be

overlapped. Thirdly, adjacent nuclei may have more

strong boundaries, than selected nucleus. Fourthly,

strong edges reflecting chromatin structure inside

the nucleus appear.

Due to the features listed above any single tech-

nique failes to solve the segmentation task properly

(Bengtsson, 2004). Currently the researches more

often turn their attention to combined techniqes.

A combined technique for automated segmenting

of cell nuclei in cytological specimen images is pro-

posed. The solution of segmentation problem is ob-

tained by combining two level active contour model

and thresholding procedure with automatically esti-

mating thresold value from image histogram in CIE

Lab colour space.

Two level active contour model (or snake) is

formed using nonlinear model of a dynamic system

in terms of state-space. A snake can be initiated in

automated and manual modes. Taking into account

the properties of the stain, segmentation at a coarse

level is operating using blue colour component in

RGB space. Correction at precise level of segmenta-

tion is made using the green component. To elimi-

nate the influence of the neighboring nuclei bounda-

ries the modified Gaussian filter based on the heat

equation with a heat source is used. In order to in-

crease the capture range of the snake the wave

propagation model is implemented.

2 METHODS FOR SEGMENTING

SPECIMEN IMAGES

One of the popular segmentation techniques in cy-

tology is thresholding with automatically estimated

threshold value (Borst, 1979). The technique is com-

putationally simple but it is effective only in case

when objects and background differ in colour or

238

M. Murashov D. (2007).

AUTOMATED COMBINED TECHNIQUE FOR SEGMENTING CYTOLOGICAL SPECIMEN IMAGES.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications, pages 238-245

DOI: 10.5220/0002071402380245

 SciTePress

gray level. In more complicated cases the segmenta-

tion consists in extraction of features per pixel and

their classification into different classes of sub-

regions. But in many cases the segmentation should

be controlled and the result should be corrected in-

teractively.

Using the simple techniques, one may run into

problems if the nuclei are clustered, the image back-

ground is inhomogeneous, and there are intensity

variations within the nuclei. By combining the

methods some of these problems can be solved. For

segmentation of cell nuclei in histological tissue

images, the method based on watersheds and dis-

tance transform (Malpica, 1997) was proposed. In

(

Bengtsson, 2004), a method for segmentation of cell

nuclei in tissue images by combining seeded water-

sheds with gradient and shape information was pre-

sented. The main disadvantage of these methods is

that one should have a tool for correcting the results

manually in complicated cases. In (Comaniciu,

2001) an approach based on nonparametric clusteri-

zation using gradient ascent mean shift procedure is

presented. The algorithm outlines clusters in Luv

space and marking their boundaries. But for obtain-

ing the suitable result the manual merging of clusters

is needed. In (Colantonio, 2006) a pixel-by-pixel

nural network classification procedure is presented.

The network is trained using clustering algorithm.

The procedure is efficient but a special tool for cor-

recting the results manually in difficult cases is

needed.

For segmentation of cell nuclei in histological

and cytological images, active contour models (pa-

rametric and geometric), or snakes, were used in

(Klemencic, 1998, Ortiz de Solorzano, 2001, Ley-

marie, 1990). Snakes provide the smooth contour

without gaps at the object boundary. Snakes were

firstly proposed in (Kass, 1987). The main idea of

parametric active contours is the following. Paramet-

ric active contour is defined as a curve

[

]

[

]

() (), (), 0,1sxsyss

=∈ (here, s is a parameter),

that moves through the spatial domain of an image

minimizing the energy functional:

() () ()

()

()()

]

1/ 2

ext

EssEsds

αν βν ν

′′′

=++

⎡

⎣

∫

(1)

where

(

)

′

and

()

′′

(the first and the second

derivatives of

(

)

with respect to s) characterize

the energy of internal forces, α and β are weighting

parameters that controls snake’s tension and rigidity,

ext

E – the energy of the external force. The external

force pulls the snake toward the object boundaries.

The functional (1) achieves its minimum at the

boundaries of the object. A gray-level image

(, )

is considered as a function of spatial coordinate

variables (x, y).

ext

E is defined as:

[]

(, ) (, ) (, )

ext

Exy GxyIxy

=−∇ ∗

(2)

where

(, )Gxy

denotes the a Gaussian kernel with

standard deviation σ, ∇ denotes the gradient opera-

tor, * denotes the convolution. As σ increases, the

blurring appears and the capture range of the active

contour increases. The curve that minimizes (1, 2)

must satisfy the Euler equation. A numerical solu-

tion of the equation can be found using an iterative

procedure which is finished when the balance of

forces is achieved. The main disadvantage of the

method is the limited capture range. The effective

solution of this problem is the Gradient Vector Flow

(GVF) method, proposed in (Xu, 1998). Within this

framework, a new external force which is the solu-

tion of the generalized diffusion equation is used.

This force minimizes the new energy functional that

includes the term compensating the lack of force

farther away from the object boundaries. Such a

model increases the capture range and provides con-

vergence to boundary concavities, but it is computa-

tionally expensive. Geometric active contours

(Caselles, 1993) are based on the curve evolution

theory and level set method. This model is less com-

putationally expensive than parametric model and

makes it possible to segment more than one object in

the image. The model provides good results for im-

ages with high contrast. When the object boundary

has gaps, the contour leaks through the boundary. A

modified model, based on the relation between ac-

tive contours and the computation of geodesics in a

Riemannian space (Sapiro, 2001), eliminates leaking

at some extent. In (Yang, 2005) authors proposed a

combined snake-based approach to segmentation of

tissue images using colour gradients in Luv space.

For snake initializing a classifier is used. Classifier

is trained using sample images selected by experts.

Thus, one may conclude that: (a) the task of de-

velopment fully automated segmentation technique

is actual; (b) only combined techniques can provide

the suitable result; (c) snakes are efficient for seg-

menting cell nuclei images and can be used in auto-

mated tools (d) snakes also provide within the same

framework an instrument for manual segmentation

in difficult cases. In the next sections the problems

concerned with the development of automated com-

bined snake-based technique are considered.

AUTOMATED COMBINED TECHNIQUE FOR SEGMENTING CYTOLOGICAL SPECIMEN IMAGES

239

3 ACTIVE CONTOUR MODEL

For developing a segmentation technique it is neces-

sary to have a model of the object boundary.

3.1 Boundary Model

In literature an object boundary is defined as an ar-

rangement of local edges. Local edges are defined as

discontinuities in image luminance from one level to

another (Pratt, 2001). Various types of edge models

are known (Rohr, 2001). In (Belyaev, 1998, Eberly,

1994) edges are defind in terms of surface theory as

a ridge of the surface produced by the function of

the gray-level gradient module computed from the

image. In (Eberly, 1994) definitions of ridges are

given in terms of extremal intencity values, in terms

of principal curvature extremal values, and in terms

of level surface. In this work the following definition

of ridges is used (Eberly, 1994).

Let function

():

hx R R→ is of the class C

Definition. Define

()WHh

−

, where H(h) is

Hessian matrix, and let

and

v , 1 in≤≤ be its

eigenvalues and eigenvectors. Assume that

...

≥≥ and 1 dn≤≤. A point x is a ridge point

of type n-d if

() 0

> and x is a generalized maxi-

mum point of type n-d for h with respect to

[ ,..., ]

Vv v= .

The function

()hx has a generalized maximum of

type n-d at x if

() 0

Vhx∇= and (())

VHhxV is

negative definite (Eberly, 1994).

Since

( ) { | | ,..., | | }

idd

V H h V diag v v

λλ

and

the eigenvalues are ordered, the test for the ridge

point reduces to

() 0

Vhx∇= and 0

> .

Let the gray image be described by the function

u(x)

∈

, u(x):R

→R

, x=(x,y)

. The coordinate

frame Oxyz is introduced; the plane Oxy is coinci-

dent with the image plain, and z=u(x,y). Let us con-

sider the function h(x):R

→R

() ()

huuu=∇ = +xx ,

(3)

where

∂

. In this case the ridge of

the surface h(x,y) will be a connected set of general-

ized maximum points of type 1 on the surface h(x,y),

vector v will be aligned with the surface principal

direction orthogonal to the ridge direction at this

point. For 2D images the following property follows

from the ridge definition (from the condition of 1-

maximum).

Property. At the ridge point of the surface h(x,y)

at least one principal direction is parallel to the co-

ordinate plane Oxy.

We consider an object in the image as a con-

nected set of points in some closed region

R⊂ .

We consider an edge in the image u=u(x,y) as a

projection of

(, )zhxy

ridge onto Oxy plane.

We consider the boundary of an object in the im-

age as a simple closed curve which includes the

edges separating the inner object regions from the

surrounding regions.

In the next section using the definitions and no-

tions given here, an active contour model will be

developed.

3.2 Active Contour Model

As the digital image includes a finite number of pix-

els, it is valid to present an active contour as a set of

n dynamic pointwise objects:

() ( ()), (0)

tfxtx x



(4)

where x=(x,y)

is the vector of spatial coordinates, t

is time. Function f(x(t))∈C

in the neighborhood of

the edge points x

should force the system (4) to

move towards x

and should provide stability with

respect to x

. As soon as we consider a set of points

modelling a continuous curve it is reasonable to say

about stability only along the normal to the contour

(or along the normal to the intensity edge).

Let us consider the function h(x,y) (3). The func-

tion z=h(x,y) defines the surface

VR⊂

. Further

on, we shall analyze the properties of the surface

z=h(x,y) in the neighborhood of points located at the

intensity edge.

Statement. If the function in the right-hand part of

the system (4) is constructed as

(, )

hh=

, the

system (4) will be stable in the neighborhood of in-

tensity edge in the sense of the first Lyapunov

method (Lee and Markus, 1971):

[

]

() 0,Re () 0

exx

fx f x

(5)

where

()

is a matrix of the linear approxi-

mation of the system (4) at x=x

Proof. The linear approximation of the system (4) in

the neighborhood of the edge point is described by

the equation:

xx xy

xy yy

⎛⎞

⎜⎟

⎝⎠

(6)

Here the system matrix is Hessian matrix. Let

O`x`y`z` be a coordinate frame with the origin at the

h(x,y) ridge point P which is projected to the edge

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

240

point x

in the Oxy plane, the axis z` is aligned with

the surface normal, the axes O`x` and O`y` are

aligned with the principal directions (it is assumed,

that the couple of quadratic forms has different ei-

genvalues). We place the origin of the frame Oxyz in

P. Let axis Ox align with the principal direction

orthogonal to the ridge direction. The local surface

structure in the neighborhood of

PV∈ is deter-

mined by its Gaussian curvature

2222

1 2 '' '' '' ' '

() ( )/(1 )

xx yy xy x y

Phhhhh

λλ

== − ++,

where

are the eigenvalues of matrix

III

−

, I

and

II are matrices of the first and the second fun-

damental quadratic forms. Gaussian curvature sign is

the same as one of Hessian matrix determinant.

The following cases are considered.

O`x`y` plane is tangent to (, )hxy surface and

coinsides with

Oxy. In this case

h = and

'' ''

xy yx

hh==. If P is the local maximum, then

< ,

< . The system (6) splits into two inde-

pendent equations. In this case, the system matrix is

the Hessian matrix, and its eigenvalues are equal to

the principal curvatures

of (, )hxy at the

point P. Hence, the system is stable according to the

first Lyapunov method.

2. O`x`y` plane is tangent to

(, )hxy surface and

coinsides with Oxy plane,

'' ''

xy yx

hh==

1''

2''yy

= . The O`x`y` plane is tangent to the surface

and parallel to the Oxy plane. In this case

and

h = . Let us assume

< ,

= (or

< ). Then the system will be stable in the sense

of the first Lyapunov method along the Ox axis and

neutral along the Oy axis. So, an object (4) will

move from some initial point (x

) to a point (0,y

3. O`x`y` plane is tangent to

(, )hxy surface and

coinsides with Oxy plane,

hh==, and

hh==

;

< ,

> , or vise versa. In this

case, P is a saddle point, and the system is stable

along one of the principal directions and tends to the

closest local maximum along another one. So, at the

steady state the pointwise objects move permanently

along the curvature lines from the

(, )hxy saddle

points towards the nearest local maxima or parabolic

points. Hence, the snake is stable with respect to the

intensity edge.

4. O`x`y` plane is tangent to

(, )hxy surface but

does not coinside with Oxy (see Figure 1). Accord-

ing to the property of ridge points, at least one of the

prncipal directions is parallel to image plane. Let

axis O`x` be in Oxy plane. Axis O`x coinsides with

Ox` and Oy axis directed along the projection of

O`y` axis onto Oxy plane. In O`x`y`z` frame the sur-

face will be described by a function

'(',')zxy

(7)

We shall find out how the Hessian matrix and its

eigenvalues will change in Oxyz frame. Coordinates

x`, y`, z` are transformed to coordinates x, y, z ac-

cording to the following expression:

10 0

0cos sin ,

0sincos

θθ

<<=−

−

⎛⎞

⎡⎤ ⎡ ⎤

⎜⎟

⎢⎥ ⎢ ⎥

⎜⎟

⎢⎥ ⎢ ⎥

⎜⎟

⎢⎥ ⎢ ⎥

⎣⎦ ⎣ ⎦

⎝⎠

Taking into account that

= and

= it is

shown that the elemets of the Hessian matrix will be

as follows:

cos

∂

∂∂

cos

∂

(8)

Figure 1: The surface z=h(x,y) and coordinate frames.

From (8) follows that theHessian is the diagonal

matrix and the signs of the eigenvalues depend on

cosθ sign. The expressions (8) can be justified by

comparing formulas for Gaussian and mean curva-

ture in both coordinate frames. Hence, as in cases

described above, the linear approximation of the

system (4) splits into two independent equations

with respect to variables x and y. According to the

conditions (5) the sysytem (4) will be stable with

rspect to intensity edge. The statement is proofed.

In practice, the function in the right-hand part of

the system (4) may be composed of several compo-

nents. The main component f

is formed as a

smoothed image edge map:

() [ () ()]

xGxhx

=∇ ∗ ,

where x=(x,y)

, G is the Gaussian kernel with stan-

dard deviation σ,

∇

is the gradient operator. The

appearance of f

(x) for 1-D case and the stages of

forming are shown in Figure 2 (a). Also, a smooth-

ing term of the form

11 max

()/| |kf x f (here

()

x char-

AUTOMATED COMBINED TECHNIQUE FOR SEGMENTING CYTOLOGICAL SPECIMEN IMAGES

241

acterizing the curvature, f

max

is the maximum value

of f

(x)) is introduced in (4). Let

()

() (), ()

Cs xs ys=

be a parameterized representation of the contour at

fixed instant of time t, here, s - is the Euclidian arc

length. We define

()

x by the expression:

() ()

xCs= ,

,()

dx dy

ds ds

⎛⎞

⎜⎟

⎝⎠

Thus, the equation describing the dynamics of the

pointwise object will be as follows:

(

)

0max11max

()/| | ()/| |

t fxf kfxf=+



(9)

Coefficient k

is calculated from the stability condi-

tions of model (9). To eliminate discontinuities and

redundant contour points during evolution, resam-

pling procedure is applied. For segmenting images

with low contrast boundaries of the objects of inter-

est and high contrast boundaries of adjacent objects,

the two-level algorithm is proposed. At the first

level, the coarse approximation of the boundaries is

obtained. At the second level, the contour evolution

results in precise boundary approximation.The initial

condition of model (9) at the first level is the given

initial contour, for example, an elipse. Initial condi-

tion at the second level is the contour obtained at the

first level. The accuracy of segmentation substan-

tially depends on nucleus edge map quality. At the

first level, where the main goal of preprocessing is

the obtaining of coarse nucleus edge map and sup-

pression of high contrast edges of adjacent nuclei,

the blue component of the input image is used. At

the second level, where contour evolution results in

precise boundary approximation, the influence of

adjacent nuclei is not crucial, but it is necessary to

operate with more precise and strong nucleus edge

map. In this case, color reduction is performed by

subtracting the green component from the input im-

age.

3.3 Expanding the Capture Range

Within the developed technique, thresholding and

subsequent Gaussian blurring are applied to the

function h(x,y) in order to strengthen and to level off

the edge map. The standard deviation σ determines

the capture range of the model (4). At large σ, the

boundaries of the objects in the analyzed image dis-

appear and the adjacent objects merge. The pre-

sented model accurately segments the objects of

simple shapes with smooth boundaries. But it fails to

segment the images with boundary concavities. Xu

and Prince (Xu and Prince, 1998) proposed the GVF

model that uses the vector field to force the snake to

move. The vector field is computed from the image

as the steady-state solution of a pair of linear partial

differential equations. The GVF model provides the

ability to move the snake into boundary concavities

but it is computationally expensive. In this paper, in

order to expand the capture range of the model (4),

the model of wave propagation is used. The main

idea is to spread the large values of the function in

the right-hand part of (4) (see Figure 2 (b)).

(a)

(b)

Figure 2: Constructing active contour model: (a) forming

component f

(x) for 1-D case, top – object boundary, mid-

dle – smoothed edge map, bottom – function f

(x); (b)

expanding the capture range of the active contour model.

Unlike the GVF model, there is no need to obtain

the steady-state solution of differential equations.

For this model, the Cauchy problem for the hyper-

bolic partial differential equation is solved with the

initial conditions w=

(, )Ghxy

, w

=0:

2222

(, ,) ( , ,),

() (,), / / ,

wxyt a wxyt

wt Ghx y x y

=Δ

Δ=∂ ∂ +∂ ∂

(10)

where

is the Gaussian kernel with standard de-

viation σ. Equation (10) describes the wave propa-

gation process generated by the smoothed edge map.

When solving equation (10) at each instant of time t

at a point (x,y) the values of

ww are calculated,

and maximal absolute values at the wave front are

stored. The sign of the stored value is the same as

the sign of the first nonzero value of

ww calcu-

lated at this point. Thus, the maximal gradient values

of the function

(, )Ghxy

propagate inside and out-

side the object boundary in natural way. The size of

this region is defined by the value of at. In result, the

vector field that forms the right hand part of model

(4) is obtained. In Figure 3 (a)-(e) an example of

segmenting of an object with non-convex boundary

is shown: (a) – initial contour approximation, (b) –

smoothed edge map, (c) and (d) -

, (e) – re-

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

242

sulting contour, bright regions correspond to positive

values, dark ones – to non-positive values. The

frame origin is at the top left corner of the image. In

Figure 3 (f)-(k) the results of cell nucleus segmenta-

tion are shown: (f) – the initial contour approxima-

tion; (g) – coarse approximation; (h) – precise ap-

proximation, (i) – blurred edge map

(, )Ghxy

; (j),

(k) –

ww at the second level.

(a)

(b)

(c)

(d)

(e)

(f)

(g) (h)

(i)

(j)

(k)

Figure 3: Image segmentation process: (a) – (e) – artificial

image, (f) - (k) – cytological image.

3.4 Modified Gaussian Filter

For the successful operating of the active contour

model two problems should be solved. Firstly,

strong edges at the boundaries of the segmented nu-

cleus should be obtained. Secondly, strong edges at

the boundaries of the adjacent nuclei should be sup-

pressed. For constructing function f

(x) in the right-

hand part of the active contour model (9) Gaussian

blur is used for image smoothing before obtaining

the edge map h(x) and then for blurring h(x).

For suppressing strong edges at the boundaries of

adjacent nucleus the modified Gaussian filter was

applied. The Gaussian kernel used in a standard

Gaussian filter is the fundamental solution of a heat

equation (Koederink, J., 1984). The modified filter is

constructed on the basis of a heat equation which

features the development of two-dimensional non-

stationary process in the fixed environment with heat

sources or sinks:

(, ,),

txxyy

uu u Fxyt−=+

() ,ut u=

0 f

ttt≤≤ ,

(11)

here,

(, ,)uuxyt

is the image under processing,

y - are the spatial coordinates, t is time, t

, t

are

initial and final moments. On the one hand, for im-

age smoothing before obtaining edge map, the func-

tion

(, ,)

xyt describing a source or sink of heat,

should be designed so that the adjacent nuclei should

not be smoothed and merge with one of interest. On

the other hand, the function

(, ,)

xyt should not

generate the strong

edges in the image edge map.

Thus, we may define this function as follows:

,( , ) int( );

(, ,)

0, ( , ) int( ),

xx yy

uuxy C

Fxyt

xy C

+∉

⎧

⎪

⎨

∈

⎪

⎩

(12)

where

int( )C denotes the set of points (, )

y inside

the initial approximation of contour

C .For blurring

the edge map the function

(, ,)

xyt should be cre-

ated so that it should essentially reduce the intensity

of the pixels outside the initial contour

C . For ex-

ample, the function may be defined as follows:

(, ,) *C[ [ ( )]]

Fxyt G FILL C

= ,

(13)

for

0 f

ttt

≤

≤ , where

C denotes the initial contour,

denotes the dilation operator with the structuring

element B, FILL denotes the fillhole operator (Soille,

2004),

is the Gaussian kernel with standard de-

viation σ, * denotes the convolution, and

C denotes

the complementation operator. The size of structur-

ing element B is set equal to σ. The initial approxi-

mation of the contour in this case should be set out-

side of the selected nucleus. In Figure 5 the stages of

constructing the function f

(x) in the right-hand part

of model (9) using the modified Gaussian filter with

different functions

(, , )

xyt are illustrated. In Fig-

ure 4 (a) the given fragment of the preparation image

with the initial contour

C is shown. In Figure 4 (b)

(, , )

xyt is shown. Figure 4 (c) the result of the

initial image blurring with function

(, , )

xyt de-

fined by (12) is presented. In Figure 4 (d) one can

see the edge map obtained from the image in Figure

4 (c). In Figure 4 (e) the result of applying morpho-

logical opening and thresholding operations to the

image in Figure 4 (d) is shown. In Figure 4 (f) the

blurred image Figure 4 (e) is presented, here the

function

(, , )

xyt is defined by expression (13).

The scheme for the numerical solution of the equa-

AUTOMATED COMBINED TECHNIQUE FOR SEGMENTING CYTOLOGICAL SPECIMEN IMAGES

243

tion (11) is based on the scheme presented in (Lin-

deberg, 1994).

(a) (b) (c)

(d) (e) (f)

Figure 4: The stages of constructing the function

(x)

using modified Gaussian filter: (a) the given fragment of

the preparation image and initial contour

C ; (b) the heat

source function

(, , )

xyt ; (c) blurred image (a), the

function

(, , )

xyt is defined as (12); (d) the edge map

obtained from image (c); (e) the result of applying mor-

phological opening and thresholding operations to image

(d); (f) blurred image (e), the function

(, , )

xyt is de-

fined as (13).

4 AUTOMATED SNAKE

INITIALIZATION

In cytological specimen image segmentation tasks a

lot of objects appeared in the image (see Figure 5)

should be segmented.

Figure 5: Cytological specimen image.

The manual snake initialization making segmenta-

tion task crucially time consuming. In (Yang, 2005)

a classifier trained by example provided by experts

is applied for obtaining rough approximation of ob-

jects used for initializing GVF snake. Taking into

account instability of staining properties and condi-

tions of specimen image aquizition the experts

should train the classifier regularly.

Figure 6: Component a of the specimen image in the CIE

Lab color space.

In this work a simple automated initialization

procedure based on the specimen staining properties

is proposed. The procedure is based on the proper-

ties of specimens stained by Romanovsky-Giemza

technique. Specimen image component a in the CIE

Lab colour space (see Figure 6) has bimodal inten-

sity histogram (see Figure 7).

Figure 7: Histogram of the image shown in Figure 6.

Using thresholding operation with automatic es-

timated threshold value one can obtain a binary

mask of the specimen image (see Figure 8).

Figure 8: Binary mask of the image shown in Figure 7.

Further on, the following operations should be ap-

plied to each of the objects taken one-by-one in the

binary mask image to obtain the corresponding ini-

tial approximation of the contour. First, morphologi-

cal fillhole operation. Second, filtering by area

value. Small objects are excluded. If the object area

is large, an iterative procedure is applied to compo-

nent a image fragment in order to find the threshold

value at which the binary object splits into parts.

After that, filtering is applied to each part of the ini-

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

244

tial object. Third, the white top-hat operation is ap-

plied to obtain contour initial approximation.

It is necessary to note, that at step 2 one may use

distance transform operation to separate two touch-

ing objects instead of described iterative procedure.

The iterative procedure is effective because of

smoothness of intensity histogram.

5 CONCLUSIONS

The combined technique for automated segmenting

of cell nuclei in cytological specimen images is pro-

posed. The solution of segmentation problem is ob-

tained by combining two level active contour model

and thresholding procedure with automatically esti-

mating threshold value from image histogram in CIE

Lab colour space. The main features of the technique

are: implementation of the wave propagation model

and modified Gaussian filter based on the heat equa-

tion with heat source, availability of coarse and pre-

cise levels of contour approximation, automated

snake initiation. The technique is successfully im-

plemented for segmenting cytological specimen im-

ages.

ACKNOWLEDGEMENTS

This work is partially supported by Russian Founda-

tion for Basic Research Grants NN 05-07-08000, 06-

01-81009, 06-07-89203, by the project within the

Program of the Presidium of the Russian Academy

of Sciences "Fundamental Problems of Computer

Science and Information Technologies", and by

INTAS Grant N 04-77-7067.

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