International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
BFO Based Active Contour for Tumor Segmentation
Manju#1, Karamjeet singh*2
#1
Student of ECE Dept.,PTU , *2Assistant Professor of ECE Dept.,PTU
Fatehgarh Sahib, INDIA
Abstract— As the image processing filed grows day by day
researcher’s moves towards bio medical filed to emerge new
fields to make detection of various medical diagnosis using
automated image processing algorithms. One of the fields from
this is tumor segmentation also know as tumor detection using
image processing algorithms. Till yes many researchers come up
with various algorithms to detect tumor automatically form xrays and ct scans. Here we are going to propose a new method to
detect tumor using bacteria forging optimization (BFO) to
optimize our area of detection. The experimental results of BFO
optimization automatically detect the Brain tumor with high
efficiency and accuracy.
Keywords— — GT(ground truth),Multi -thresholding
value.
, fitness
I. INTRODUCTION
The body is made up of many types of cells. Each cell
perform special functionality.i.e cell has the property to grow
and then divide in an orderly way to form new cells to keep
the body healthy and working properly. If the cells goes out
of order then the body will not work properly. if they divide
frequently and without any order Then the formation of extra
cells form a mass of tissue called a tumor. Tumors are benign
or malignant. Brain is the main operator of the body and
allows us to adjust with our environment.. However, the
problem is ill posed due to the enormous variability of tumors
both in terms of location as well as in terms of geometric
characteristics and progression. Previous medical image
segmentation techniques adopt prior knowledge to overcome
the ill-posedeness of the task which was hard and less
efficient.
One of the most difficulties in tumor excisions and tissue
differentiation is the border and cells overlapping between
normal and abnormal tissues in gray level of the medical
images and that are the challenge of the surgeon or physician
to distinguish that. The surgeon must be very accurate and
careful to remove that tumor without causing a damage for
nearby tissue. If the surgeon has the accurate measurements
and location of the involved tissue he can do his job more
accurately; there are some new medical instrument used to
remove the tumor specially in the brain, like Brainlab
instrument (Navigator), as well as Linear accelerator (LINAC)
these devices need well defined dimensions of abnormal tissue
for extraction. These instruments works without opening a
large area in the scalp depending on the image only. There is a
need to detect the tumor area and location in the brain very
accurately and hence BFO is one of the optimization
technique to overcome this situation very effectively.
ISSN: 2231-5381
II. METHODOLOGY
A. Histogram Equalization
Histogram Equalization is necessary to enhances the
contrast and transforms the values in an intensity image so
that the histogram of the output image approximately matches
a specified histogram. it can be seen by the Fig. 1.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
Fig.4 Segments marked on an Original image
Fig. 1 Histogram Equalised image of Brain
B. Segmentation With Multi- Thresh Holding
Thresholding is a widely applied step for image
Segmentation . properly thresholded image leads to better
segmentation. often the stress of over segmentation is on the
threshold operation. Thresholding is of two types : Global and
Local. in the global thresholding technique, a grey scale image
is converted to binary image based on an image intensity
value.In the local thresholding technique , a threshold surface
is constructed that is a function on the image domain. To
segment an image let an image is the combination of different
blocks. Difference can be clearly seen in Fig. 2 representing
an original image and Fig. 3 representing the image with
segmentation.
Fig. 2 Original Image
of Brain
D. Feature Extraction
Feature extraction is used to extract the different features of
an image. Here we are extracting the Area, and colour Of each
detected object. For an example for our given image the
FVT(Feature Vector Table) is given Below.
As shown in Table I FVT number 1 to 26 represent the total
number of tumor a object detected in reference picture of
Brain, area of each object and the R,G,B value of each object
respectively.
E. BFO Implementation:
Bacterial forging optimization algorithm is used to detect
the tumor and the location of tumor in the brain .it is a very
effective algorithm to detect the tumor. After calculating
FVT, BFO is implemented by population and velocity
initialization. Then a fitness value is calculated using fitness
function. Then a selection process takes place. If it selects the
true fitness value then it moves forward and predicts tumor
part and its parameters like SN, SP, and ACC .But if it does
not select the desired fitness value it will be move in a loop
until it select that particular value. When the BFO algorithm is
used for optimization then it predicts the tumor part in the
brain .it can be clearly seen in Fig. 5 which is a original image
and Fig.6 which represents the location of tumor in the brain.
Fig. 3 Multithresholded image of
Original image
C. Marking
Marking is an important step and difficult process in image
processing .it is very essential part of image analysis and
object recognition. In this module marking of objects is done.
So that total number of brain objects can be tested separately
to detect the tumor. Marking is also necessary to determine
the boundary of each object clearly. It can be easily visualized
in the below Fig. 4.
Fig. 5.Original image
Fig.6. tumor location in Brain
TABLE I. . Feature Vector Table of Reference image
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
Total noof object
detected
Area
R
G
B
mean
1
75
96.6933
96.6933
96.6933
96.6933
2
75
84.6267
84.6267
84.6267
84.6267
3
2932
135.4263
135.4263
135.4263
135.4263
4
173
77.8439
77.8439
77.8439
77.8439
5
130
73.8846
73.8846
73.8846
73.8846
6
86
104.6512
104.6512
104.6512
104.6512
7
422
135.5047
135.5047
135.5047
135.5047
8
55
103.2909
103.2909
103.2909
103.2909
9
73
96.5479
96.5479
96.5479
96.5479
10
79
183.7215
183.7215
183.7215
183.7215
11
265
131.483
131.483
131.483
131.483
12
239
3.4854
3.4854
3.4854
3.4854
13
409
124.6528
124.6528
124.6528
124.6528
14
264
226.9962
226.9962
226.9962
226.9962
15
59
128.3898
128.3898
128.3898
128.3898
16
61
128.8197
128.8197
128.8197
128.8197
17
228
134.057
134.057
134.057
134.057
18
629
135.8092
135.8092
135.8092
135.8092
19
124
80.2581
80.2581
80.2581
80.2581
20
129
133.124
133.124
133.124
133.124
21
53
29.2868
29.2868
29.2868
29.2868
22
212
102.8774
102.8774
102.8774
102.8774
23
56
137.5
137.5
137.5
137.5
24
366
131.7541
131.7541
131.7541
131.7541
25
99
86.40404
86.40404
86.40404
86.40404
26
119
81.1092
81.1092
81.1092
81.1092
27
28
29
III. PARAMETER ANALYZED (SN,SP,ACC)
ISSN: 2231-5381
BFO, is implemented on 30 different MRI images, and it
provide 99.97% accuracy and 92.55% sensitivity and
specificity 100%.the Table 3.1 represents the parameter of 30
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
different images and Fig.7 represents the images for which
parameters are to be analyzed.
Fig.7. Thirty Different images of Brain
TABLE II. Parameter Analysis of thirty different images
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
image no.
SN
SP
ACC
Pic_ 1
65.7459
100
99.857
Pic_ 2
87.963
100
99.97
Pic_ 3
88.8889
100
99.9677
Pic_ 4
97.8355
100
99.9769
Pic_ 5
89.5349
100
99.9792
Pic_ 6
97.3684
100
99.9908
Pic_ 7
98.324
100
99.9792
Pic_ 8
91.129
100
99.9746
Pic_ 9
96.2366
100
99.9331
Pic_10
99.4169
100
99.9954
Pic_11
93.3333
100
99.9723
Pic_12
98.4756
100
99.9885
Pic_13
91.0448
100
99.9723
Pic_14
97.5806
100
99.9931
Pic_15
99.8103
100
99.9792
Pic_16
99.3035
100
99.9839
Pic_17
91.2568
100
99.9631
Pic_18
83.1858
100
99.9562
Pic_19
95.1768
100
99.9654
Pic_20
96.0432
100
99.9493
Pic_21
94.7712
100
99.9446
Pic_22
87.1795
100
99.9769
Pic_23
98.5294
100
99.9977
Pic_24
88.1944
100
99.9608
Pic_25
91.7197
100
99.97
Pic_26
95.8678
100
99.9885
Pic_27
91.4286
100
99.9723
Pic_28
84.6154
100
99.9723
Pic_29
Pic_30
92.233
94.1667
100
100
99.9815
99.9839
Average
92.55%
100
99.97%
it is also representing the tumor location for the reference
image.
Fig. 8:Tumor cell of marked GT
Fig9 .Tumor part of predicted area
after BFO implementation
IV. RESULT CONCLUSION:
In this paper, proposed new method to detect tumor using
bacteria forging optimization (BFO) to optimize our area of
detection. Multi thresh holding is used to stop the contour at
desired object boundary to overcome over segmentation
problem. the proposed method is more efficient and is less
error sensitive. the effectiveness of the proposed method is
well demonstrated by the experimental result. In future, we
will explore more advanced classification techniques and
contour segmentation strategies.
Fig. 8 is the ground truth(GT) marked by expertise to locate
the tumor and Fig. 9 is the result after BFO implementation.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
[10] K. S. Angel Viji , Dr J. Jayakumari “Automatic Detection of
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