An Counting and Segmentation method of Blood Cell Image with

An Counting and Segmentation method of Blood Cell
Image with Logical and morphological Feature of Cell
Yi-De Ma1,2 , Ro-Lan Dai1 , Li Lian2 , Zai-Fen Zhang2
1. The State Key Laboratory of Arid Agroecology, Lanzhou University, Lanzhou, Gansu,
China 730000
2. School of Information Science & Engineering, Lanzhou University, Lanzhou, Gansu,
China 730000
Email: [email protected]
Abstract A new, simple method of counting and
segmenting cell image is suggested in this paper. It is
based on the feature of cell’s logical and
morphological information. By using of mathematical
morphological logical operation and laplacian filter,
the method is realized with the MATLAB 5.10. The
effect of this algorithm is tested with blood cell image
in this article and the result is desirable.
Index terms
logical operation, cell image,
morphological operation, laplacian filter
1. INTRODUCTION
Cell image’s quantitative analysis is used in the
study of biology and medicine science, for the study
of cell structure and its behavior is one of the most
important things in areas such as cell embryology,
wound healing, host defense mechanisms, and
mechanisms of tumor cell metastasis and invasion [1].
The very important step in these areas is
automatically counting and segmenting cytological
image. Because of the cell’s complex texture (the
unevenness of gray level intensity and low contrast) it
is very difficult to count and segment cell from its
background automatically [2] [3] [4 ] [5] [6] [7] . H.-S.Wu
suggested an optimal parametric segmentation of
cervical cell image [7], Dwi Anoraganingrum gave a
method to segment melanom and lymphocytes cell
image with median filter and mathematical
morphology operation [1] and Keng Wu set forth the
method of live cell image’s approximation threshold
segmentation [2] . All these segmentation algorithms
usually work for only limited types of images,
because they must exploit the special properties of
images, and a prior knowledge of the cell
characteristics should be full used; these
characteristics include the cell shape, size and
intensities relative to its background [7]. Here, in this
paper a new method of counting and segmenting
blood cell image with cell’s feature-based logical and
morphological operation is suggested. The result of
experiment is desirable.
2.THE METHOD
First, the method suggested here gets the logical
and morphological feature of blood cell image with
feature-based logical and morphological operation,
then distinguishes the segmented cells from their
background, counts the number of cells according to
this feature, and at last segments a special cell from
its neighborhood. All the processes are as following:
. Noise and Artifact Suppression
. Blood Cell Feature Extraction
. Segmenting Blood Cell From Its Background
. Counting Blood Cell Number
. Segmenting A Special Cell from Its Neighborhood
2.1Noise and Artifact Suppression
The method is realized with the help of MATLAB
5.10. Noise and artifact produced in processing of
microscope film are removed from blood cell image
with median filter or average filter.
Here, Fig.2-1 (a) is an original blood cell image,
and then the noise and artifact are suppressed in
Fig.2-1(b).
2.2Blood Cell Feature Extraction
Fig. 2-1(a) is an original blood cell image, there
are some bright dots on all cells in this image, these
are logical and morphological characters of blood cell
image and these features make it possible to segment
blood cell from its background. These features are
extracted with the method suggested here. First, the
cell image is filtered with laplacian filter (Fig.2-1(c))
and then changed into binary image A4 with the
mean of filtered image as its threshold (Fig.2-1(g)).
The original image A2 is changed into binary image
in the same way and A5 is a logical NOT image of
binary image A2, then the feature-based logic image
A7 which has the bright dots on blood cell (Fig.2-1
(d)) is gotten by using logical AND between A4 and
A5:
A6=A5&A4; logical AND between A5 and A4,
shown in Fig.2-1(f)
(a)
(b)
(e)
(f)
(c)
(d)
(g)
(h)
Fig.2-1.Blood Cell Image with Feature-based Logic
(a)original image, (b)noise suppressed image, (c)laplacian filtered image, (d)bright dots
image, (e)segmented blood cell binary image, (f)the image of A6, (g)binary image A4, (h)A
special cell binary image.
A7=erode (A5)&(A6==0)
(2.2-1)
Here the morphological operation erode of A5 in
equation (2.2-1) makes it possible to isolate the
edges of cells as well as bright dots from the
background of blood cell image A6 (Fig.2-1 (g)) and
only select areas where bright dots of cell are zero on
blood cell.
as 8 which means that any 8-connected object (such
as blood cell) in image SEGMCELLS will be counted.
At last the NUM=28, the number of blood cells in
image Fig.2-1 (a), is gotten from MATLAB 5.10 very
easily.
2.3 Segmenting blood Cell
Also the Matlab functions, find(X) and
bwselect(X,n), are used to segment a special cell, for
example, the 6th cell is segmented from its
neighborhood:
S6=bwslect (A5, c, r, 8);
(2.5-1)
where [r, c]=find(L==6) is the number of row and
column of 6th cell.
Fig.2-1 (h), S6, is a picture of binary image of 6th
cell in Fig.2-1 (a).
According to the bright dots on blood cell, the
logical and morphological characters of blood cell, it
is very easy to segment blood cell from its
background with Matlab function bwselect(A5, r, c),
which selects blood cell in binary image. That is:
SEGMCELLS=bwselect (A5, r, c); (2.3-1);
Where
[r , c]=find (A7) is the Matlab fuction, and it gets
the number of the row and column of bright dots on
blood cell.
Here binary blood cells are segmented from its
original image very simply and shown in Fig.2-1(e),
which makes it possible to continue quantitative
analysis subsequently.
2.4 Counting blood cell number
The segmented blood cell image Fig.2-1(e) and
the function bwlabel (S2, n) of MATLAB 5.10 are
used to count the number of cells automatically:
[L, NUM]=bwlabel (SEGMCELLS, 8); (2.4-1)
Here the function, BWLABEL, labels blood cell in
binary image, Fig.2-1(e).
Because of the irregular shape of cell, n is selected
2.5 Segmenting a special cell
3.CONCLUSION
A feature-logical and morphological method of
counting and segmenting blood cell image is
suggested in this paper, it uses the image processing
toolbox of MATLAB 5.10 and gets the desirable
results. The method is very simple and reliable for
animal cell image such as blood cell image in this
paper.
4. DISCUSSION
RESEARCH
AND
FUTURE
This method is very useful for cell image where
the cells don’t contact closely. But for the plant
embryo cell image, it is very difficult to use this
method. It is our next work to study the effective
method of counting and segmenting plant embryo
cell image where the cells overlap or contact closely
with each other.
REFERENCE
[1] Dwi Anoragaingrum “Cell Segmentation with
median Filter and Mathematical morphology
Operation”
0-7659-0040-4199$10.00
1999.IEEE
[2] Keng Wu etc “Live Cell image Segmentation”
IEEE trans On Biomedical Engineering, Vol.42,
No.1 1995
[3] Mark B.Jeacocke etc “A Multi-resolution
Algorithm
for
Cytological
Image
Segmentation” 0-7803-2404-8/94/$4.001994
IEEE
[4] WeiLin.etc “A Cmputational Intelligence System
for
Cell
Classification”
0-7803-49733/98/$10.00 1994 IEEE
[5] Alberto
Diaspro.etc
“Characterizing
Biostructures and Cellular Events in 2D/3D
(Using wide-field and Confocal Optical
Sectioning Microscopy)” 0-739-5175/96/$5.00
1996 IEEE Engineering in Medicine and Biology
January/February
[6] G.Fernandez.etc “A New Plant Cell Image
Segmentation Algorithm” proc. of the 8th
Int.Conference On Image Analyze and
Processing San Remo Italy pp.229-224,1315,1995
[7] H.-S.Wu.etc “Opticmal Segmentation of Cell
Images” IEEE Proc.-Vis. Image Signal
Processing. Vol.145, No.1.1998
[8] Kostas Haris etc. “ Hybrid Image Segmentation
Using Watersheds and Fast Region Merging”
1057-7149/98$10.00 1998 IEEE trans On
Image Processing, Vol.7, No.12. 1998
[9] Chang Wen Chen etc. “Image Segmentation Via
Adaptive K-Mean Clustering and KnowledgeBased
Morphological
Operations
With
Biomedical Application” 1057-7149/98$10.00
1998 IEEE trans On Image Processing, Vol.7,
No.12.1998
[10] John etc. “Image Segmentation and Anlysis Via
Multiscale Gradient Watershed Hierarchies”
1057-7149/98$10.00 1999 IEEE trans On
Image Processing, Vol.8, No.1.1999
[11] Rejean Plamonda etc. The Segmentation of
Cursive Handwriting: An Approach Based on
Off-line Recovery of the Motor-temporal
Information $10.00 1999 IEEE trans On Image
Processing, Vol.8, No.1.1999
[12] LU Zong-qi .etc “Edge Thinning Based on Sobel
Operator” Journal of Image and Graphics
Vol.5(A), No.6.2000
[13] WEI Zhi-cheng. etc. “A Study on Image
Segmentation by a New Adaptive Algorithm”
Journal of Image and Graphics Vol.5(A), No.3.
2000