chapter v brain tumor detection using...

CAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRI

BRAIN TUMOR DETECTION USING HPACO

145

CHAPTER V

BRAIN TUMOR

DETECTION USING

HPACO



146

CHAPTER 5

DETECTION OF BRAIN TUMOR REGION USING HYBRID

PARALLEL ANT COLONY OPTIMIZATION (HPACO) WITH FCM

(FUZZY C MEANS)

5.1 PREFACE

The Segmentation of Brain Tumor from Magnetic Resonance Image is an

important but time-consuming task performed by medical experts. The digital Image

processing community has developed several segmentation methods.

Four of the most common methods are:

1. Amplitude Thresholding

2. Texture Segmentation

3. Template Matching

4. Region-Growing Segmentation.

Segmentation is the second stage where Optimization forms an important part of

our day to day life. Many scientific, social, economic and engineering problems have

parameter that can be adjusted to produce a more desirable outcome. Over the years,

numerous techniques have been developed to solve such optimization. This study

investigates the most effective optimization method, known as Hybrid Parallel Ant

Colony Optimization (HPACO) is introduced in the field of Medical Image Processing.

Hybrid Parallel Ant Colony Optimization (HPACO) algorithm is a recent population-

based approach inspired by the observation of real Ant’s Colony and based upon their

collective behaviour.



147

In HPACO, solutions of the problem are constructed within an iterative process,

by adding solution components to partial solutions. Each individual ant constructs a part

of the solution using an artificial pheromone, which reflects its experience accumulated

while solving the problem, and heuristic information dependent on the problem

FCM Algorithm is one of the popular Fuzzy Clustering algorithms which are

classified as constrained Soft Clustering algorithm. A Soft Clustering Algorithm finds a

soft partition of a given data set by which an element in the data set may partially belong

to multiple clusters.

The suspicious region is segmented using algorithm HPACO. A New CAD

System is developed for verification and comparison of brain tumor detection algorithm.

Hybrid Parallel Ant Colony Optimization determine the threshold value of given image

to select the initial cluster point then the clustering algorithm Fuzzy C Means calculates

the optimal threshold for the brain tumor segmentation.

5.2 FUZZY C MEANS

Segmentation is one of the first and most important tasks in image analysis and

computer vision. In the previous works, various methods have been proposed for object

segmentation and feature extraction, described in [67, 40]. However, the design of robust

and efficient segmentation algorithms is still a very challenging research topic, due to the

variety and complexity of images. Image segmentation is defined as the partitioning of

an image into nonoverlapped, consistent regions which are homogeneous in respect to

some characteristics such as intensity, color, tone, texture, etc.



148

The image segmentation can be divided into four categories: (i) thresholding (ii)

clustering (iii) edge detection and (iv) region extraction. In this paper, a clustering

method for image segmentation will be considered.

Clustering is a process for classifying objects or patterns in such a way that

samples of the same cluster are more similar to one another than samples belonging to

different clusters. There are two main clustering strategies: the hard clustering scheme

and the fuzzy clustering scheme. The conventional hard clustering methods classify each

point of the data set just to one cluster. As a consequence, the results are often very crisp,

i.e., in image clustering each pixel of the image belongs just to one cluster.

However, in many real situations, issues such as limited spatial resolution, poor

contrast, overlapping intensities, noise and intensity in homogeneities reduce the

effectiveness o hard (crisp) clustering methods. Fuzzy set theory [75] has introduced the

idea of partial membership, described by a membership function. Fuzzy clustering, as a

soft segmentation method, has been widely studied and successfully applied in image

clustering and segmentation [53, 85, 90].

Among the fuzzy clustering methods, fuzzy c-means (FCM) algorithm

[89,150,151] is the most popular method used in image segmentation because it has

robust characteristics for ambiguity and can retain much more information than hard

segmentation methods [105,152]. Although the conventional FCM algorithm works well

on most noise-free images, it is very sensitive to noise and other imaging artifacts, since

it does not consider any information about spatial context.



149

Smoothing step has been proposed in [124,130,146]. However, by using

smoothing filters important image details can be lost, especially boundaries or edges.

Moreover, there is no way to control the trade-off between smoothing and clustering.

Thus, many researchers have incorporated local spatial information into the original

FCM algorithm to improve the performance of image segmentation [109,136,153]

Noordam et al proposed a geometrically guided FCM (GG-FCM) algorithm, a

semi-supervised FCM technique, where a geometrical condition is used determined by

taking into account the local neighborhood of each pixel [93].

Pham modified the FCM objective function by including a spatial penalty on the

membership functions. The penalty term leads to an iterative algorithm, which is very

similar to the original FCM and allows the estimation of spatially smooth membership

functions [102,103,104].

Ahmed et al proposed FCM_S where the objective function of the classical FCM

is modified in order to compensate the intensity in homogeneity and allow the labelling

of a pixel to be influenced by the labels in its immediate neighborhood. One

disadvantage of FCM_S is that the neighborhood labelling is computed in each iteration

step, something that is very time-consuming [2, 3]. Chen and Zhang proposed FCM_S1

and FCM_S2, two variants of FCM_S algorithm in order to reduce the computational

time. These two algorithms introduced the extra mean and median-filtered image,

respectively, which can be computed in advance, to replace the neighborhood term of

FCM_S. Thus, the execution times of both FCM_S1 and FCM_S2 are considerably

reduced [19, 22].



150

Szilagyi et al proposed the enhanced FCM (EnFCM) algorithm to accelerate the

image segmentation process. The structure of the EnFCM is different from that of

FCM_S and its variants. First, a linearly-weighted sum image is formed from both

original image and each pixel’s local neighborhood average gray level. Then clustering is

performed on the basis of the gray level histogram instead of pixels of the summed

image. Since, the number of gray levels in an image is generally much smaller than the

number of its pixels, the computational time of EnFCM algorithm is reduced, while the

quality of the segmented image is comparable to that of FCM_S [114,154]. More

recently, Cai et al. Proposed the fast generalized FCM algorithm (FGFCM) which

incorporates the spatial information, the intensity of the local pixel neighborhood and the

number of gray levels in an image. This algorithm forms a nonlinearly-weighted sum

image from both original image and its local spatial and gray level neighborhood. The

computational time of FGFCM is very small, since clustering is performed on the basis

of the gray level histogram. The quality of the segmented image is well enhanced [19].

Fuzzy C-Means (FCM) Algorithm

The fuzzy c-means (FCM) clustering algorithm was first introduced by

Dunn [40] and later extended by Bezdek [67]. The algorithm is an iterative

clustering method that produces an optimal partition by minimizing the

weighted within group sum of squared error objective function Jm .

( ) )1(2

1 1

ji

N

i

c

j

m

jim vxduJ -=åå= =

where thethi pixel is the center of the local window and the

thj pixel represents the



151

set of the neighbors falling into the window around the thi pixel.

( )ji pp - are the coordinates of pixel i and ix is its gray level value. gsandll are two

scale factors playing a role similar to factor a in FCM, and is is defined

m

N IRxxxN =Ì= ),......,{ 21 is the data set in the m -dimensional vector space, N- is

the number of data items, c is the number of clusters with , jiNuc <£2 is the

degree of membership of ix in the thj cluster, m is the weighting exponent on each

fuzzy membership iv is the prototype of the center of cluster )(, 2

ji vxdj - is a

distance measure between object ix and cluster center jv A solution of the object

function mj can be obtained through an iterative process, which is carried as

follows

1) Set values for c, m ande .

2) Initialize the fuzzy partition matrix .)0(U

3) Set the loop counter 0=b .

4) Calculate the c cluster centers )(b

jv with )(bU .

( )

( ))2(

1

)(

1)(

å

å

=

==N

i

mb

ji

N

i

i

mb

ji

b

j

u

xu

v

5) Calculate the membership matrix)1( +bU .

)3(1

1

1/2

)1(

å=

-+

÷÷ø

öççè

æ=

c

k

m

ki

ji

b

j

d

du

6) If e<+ }max{ )1()( bb UU then stop, otherwise, set 1+= bb and go to step 4.

Figure 5.1 Fuzzy C Means Algorithm



152

Figure 5.2 Flow Diagram for Fuzzy C Means Algorithm

Start

( )

( )å

å

=

==N

i

mb

ji

N

i

i

mb

jib

j

u

xu

v

1

)(

1)(

Initialize c, m ande

matrix .)0(U

)3(1

1

1/2

)1(

å=

-+

÷÷ø

öççè

æ=

c

k

m

ki

ji

b

j

d

du

max

{U(b)

U(b+1)

} < ɛ

Stop

Yes

No



153

5.3 PREVIOUS WORK

Corina et al. studied Active Contour Model for segment the brain MRI images

successfully [27]. Mao et al. designed an automatic segmentation method using Fuzzy k-

means, Ant colony optimization for process the optimal labeling of the image pixels [78].

Dana et al. designed a method on 3D Variation Segmentation for process due to the high

diversity in appearance of tumor tissue from various patients [28,68]. Jayaram et al.

represented Fuzzy Connectedness and Fuzzy sets used to develop the concept of fuzzy

connectedness directly on the given image for facilitating the image segmentation

[60,61]. Hideki et al. specified a technique for Partition the image space into meaningful

regions [54].

Kabir et al. prescribed a method Markov random field model for segmenting

stroke lesions on MR Multi sequences [64]. Leung et al. presented Contour Deformable

Model for segmenting required region from MRI [76]. Marcel Prastawa said VALMET

Segmentation validation tool is used to detect intensity outliers and dispersion of the

normal brain tissue intensity clusters [79,80,81,82]. Tang et al. presented Multi

resolution image segmentation. For segmenting the brain tissue structure from MRI

[117]. Pierre et al. prescribed Atlas-based segmentation for Propagation of the labeled

structures on to the MRI [94,106]. Jayaram et al described a method on Evaluating

Image Segmentation Algorithm for segmenting objects from source image [60, 61].

Jaffrey et al designed a new method semi automatic segmentation method on volume

tracking for estimate tumor volume with process [62,63].



154

5.4 SEGMENTATION BY HYBRID PARALLEL ANT COLONY

OPTIMIZATION ALGORITHM

The MRI image is stored in a two-dimensional matrix and a kernel is extracted

for each pixel. A unique label is assigned to the kernels having similar patterns. In the

labeling process, a label matrix is initialized with zeros. The size of the label matrix is

equal to the size of the MRI image. For each pixel in the image, the label value is stored

in the label matrix at the location corresponding to its central pixel coordinates in the

gray level image[26,36,37,38,39,46,146].

5.4.1 MARKVOV RANDOM FIELD

A pattern matrix is maintained to store the dissimilar patterns in the image. For

each pixel, a kernel is extracted and the kernel is compared with the patterns available in

the pattern matrix. Once it finds any matches the same label value is assigned to the

currently extracted kernel. .The labels are assigned integer values starting with one and

incremented by one whenever a new pattern occurs [71]. Finally the pattern matrix

contains all the dissimilar patterns in the image and the corresponding label values are

also extracted from the label matrix. For each pattern in the pattern matrix, the posterior

energy function value is calculated using the following formula.

( ) ( )9

å 2 2U x = { ([(y -μ) / (2*σ )]+ log(σ))+V x }i

i=1

Where, y is the intensity value of pixels in the kernel, m is the mean value of the

kernel, s is the standard deviation of the kernel, V is the potential function of the kernel

and x is the label of the pixel. If x1is equal to x2 in a kernel, then V (x) = b, otherwise 0,

where b is visibility relative parameter (b ≥ 0).



155

The challenge of finding the Maximum A Posterior (MAP) estimate of the

segmentation is to search for the optimum label which minimizes the posterior energy

function U(x). In this section a new effective approach, HPACO is proposed for the

minimization of MAP estimation.

HPACO is applied to find the optimum label from the pattern matrix. Initially,

the dissimilar patterns, the corresponding labels and the MAP values are stored in a

solution matrix and the parameters such as number of iterations (NI), number of ants

(NA), initial pheromone value (T0) are assigned the values of 100, 20 and 0.005

respectively. Also the solution matrix contains separate columns for pheromone and flag

values of each ant. The flag value is used to indicate whether the kernel has been selected

previously or not. Initially all the flag values are set to zero and the pheromone values

are assigned T0. At the initial step, all the ants are assigned random kernels and the

pheromone values are updated.

The posterior energy function value for all the selected kernels from each ant is

extracted from the solution matrix. Compare the posterior energy function value for all

the selected kernels from each ant, to select the minimum value from the set, which is

known as ‘Local Minimum’ (Lmin) or ‘Iterations best’ solution. This local minimum

value is again compared with the ‘Global Minimum’ (Gmin). If the local minimum is

less than the global minimum, then the local minimum is assigned with the current global

minimum. Then the kernel that generates this local minimum value is selected and its

pheromone is updated.



156

The pheromone value for the remaining kernels is updated. Thus the pheromone

values are updated globally. This procedure is repeated for all the image pixels. At the

final iteration, the Gmin has the optimum label of the image. The corresponding kernel is

selected from the pattern matrix. The intensity value of the center pixel in the kernel is

selected as optimum threshold value for segmentation. In the MRI image, the pixels

having lower intensity values than the threshold value are changed to zero. The entire

procedure is repeated for any number of times to obtain the more approximated value.

Step 1: Read the brain image or the stored in a two dimensional matrix.

Step 2: Divide the image to 3x3 labels (cells).

Step 3: For each label in the image, calculate the posterior energy U (x) value.

U(x)={Σ[(y-μ)2

/(2*σ2

)]+Σ log(σ)+ΣV(x)} Where

y = intensity value of pixels in the kernel, μ = mean value of the kernel,

σ = standard deviation of the kernel, V = potential function of the kernel, and

x = center pixel of the label. If x1 is equal to x2 in a kernel, then

V(x) = β, otherwise 0, where β is visibility relative parameter (β≥0).

Step 4: The posterior energy values of all the labels are stored in a separate matrix.

Step 5: HPACO System is used to minimize the posterior energy function. The

procedure is as follows:

Step 6: Initialize the values of number of iterations (N), number of ants (K) for colonies,

initialize number of colonies (M), initial pheromone value (T0), a constant value for

pheromone update (ρ). [Here, we are using N=20, K=10, M= 10, T0=0.001 and ρ=0.9].

Step 7: initliasition for each colonies. {

Step 8: slave colonies systems

{ colony 1, colony 2…. Colony M-1 }



157

Algorithm of colony 1

Mij¬ Original Image

for each pixel in Mij

G ¬ kernel of the border pixel of size 3´3 from M

U ¬ fitness value; the posterior energy U (x) is calculated.

U (x) ={å[( y-m )2 / ( 2 * s2

)] +å log(s) + å V(x)}

end

N ¬ 50; K¬ 10; T0¬ 0.001; r ¬ 0.9

S ¬ {U(x),T0, flag} flag column mentions whether the pixels is selected by the ant

or not. Store the energy function values in S. Initialize all the pheromone values

with

T0=0.001. repeat for N times

for each pixel in Mij

for each ant

gi¬ a random kernel for each ant, which is not selected previously.

Tnew ¬ (1–r) * Told + r * T0 for gi

End Lmax ¬ max(Ui(x))

if (Lmax < Gmax) then Gmax = Lmax

g¬ Select the ant, whose solution is equal to local maximum

Tnew ¬ (1 – a) * Told + a * DTold, only for g

End , End

Similarly this algorithm is used to M-1 slave ant colonies and also master ant

colonies system

}



158

Step 8: The final highest global value derived from slave ant colony system.

Step 9: Master colony system

Step 10: The master colony yield global optimum value

Step 11: compare to 8 and 10, the highest global optimum value treated as a

optimum threshold value.

The Gmin has the optimum label which minimizes the posterior energy function.

Step 12: The optimal value HPACO is used to select the initial cluster point.

FCM- HPACO Algorithm is the following:

Step 13: Calculate the cluster centers.

C = (N/2)1/2

Step 14: Compute the Euclidean distances

Dij = CCp – Cn

Step 15: Update the partition matrix

æ öç ÷è ø

å ij

kj

ij 2/(m-1)c

k=1

d

d

1U = (Repeat step 4) Until Max[ │Uij(k+1)-Uijk│] < € is

satisfied

Step 16: Calculate the average clustering points.

c c nn 2

iji i ij

i=1 i=1 j=1

C = J = U då åå

Step 17: Compute the adaptive threshold

Adaptive threshold =max (Adaptive threshold, ci ) i=1...n

Figure 5.3 HPACO with FCM



159

In the MRI image, the pixels having lower intensity values than the adaptive

threshold value are changed to zero. The entire procedure is repeated for in the MRI

image, the pixels having lower intensity values than the adaptive threshold value are

changed to zero. The entire procedure is repeated for any number of times to obtain the

more approximated value.

5.5 IMPLEMENTATION OF HPACO WITH FCM

After completing all the process the generated output is given to the FCM as

input. The optimal value of HPACO through MRI Brain Image is given as an input for

FCM. The aim of FCM is to find cluster centres (centroids) that minimize dissimilarity

function.

The membership matrix (U) is randomly initialized as

c

ij

i=1

U =1;å

Where i is the number of cluster

j is the image data point

The dissimilarity function can be calculated with this equation

c c nn 2

iji i ij

i 1 i 1 j 1

C J U d= = =

= =å åå

Where

Uij - is between 0 and 1

Ci - is the centroid of cluster i

dij - is the Euclidean distance between ith and centriod (Ci ) and jth data

point

M - is a weighting exponent.



160

To calculate Euclidean distance (dij)

Euclidean distance (dij) = Cluster center pixels - current neuron

dij = CCp – Cn

where

CCp - is the Cluster center pixels

Cn - is the current neuron

i.e. Number of clusters is computed as

C = (N/2)1/2

where

N= no. of pixels in image

To find the Minimum dissimilarity function can be computed as

ij

kj

ij 2/(m-1)c

k=1

d

d

1U =

æ öç ÷è ø

å

where

dij=|| xi -cj|| and dkj=|| xi –ck||

xi - is the ith of d- dimensional data

cj - is the d-dimensional center of the cluster x

so these iteration will stop when the condition

Max ij {│Uij(k+1)

-Uijk│} <€ is satisfied

where

€ - is a termination criterion between 0 and 1

K - is the iteration step



161

The step of the FCM Algorithm has been listed

Step 1: Initialize U = Uij matrix

Step 2: At K step initialize centre vector C (k)

= C j taken from HPACO Clustering

Algorithm

Step 3: Update U (k)

, U (k+1)

, then compute the dissimilarity function

ij

kj

ij 2/(m-1)c

k=1

d

d

1U =

æ öç ÷è ø

å

If || U (k+1)

- U (k)

|| < € then stop. Otherwise return to step3.

Figure 5.4 FCM Algorithm

5.6 EXPERIMENTS AND RESULTS

The sliding window of 3×3, 5×5, 7×7, 9×9 and 11×11 are analyzed. In that 3×3

window is based on the high contrast value than 5×5, 7×7, 9×9, and 11×11. The

following table shows the adaptive threshold value, no. of pixels in the tumor region,

execution time and weight vector. The execution time in HPACO with FCM, the 3x3 is

14, 5x5 is 28, 7x7 is 25, 9x9 is 23 and 11x11 is 18 ,Adaptive threshold for HPACO with

FC M is 3x3 is 185, 5x5 is 164, 7x7 is 160, 9x9 is 148 and 11x11 is 139, the number of

segmented pixel in HPACO with FC M of 3x3 is 1000, 5x5 is 1995, 7x7 is 2285, 9x9 is

3445and 11x11 is 8881,Weight vector for HPACO with FCM is 3x3 is 14, 5x5 is 28, 7x7

is 25, 9x9 is 23 and 11x11 is 18 are shown in Fig 5.5.

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

162

Tab

le 5

.1 P

erfo

rm

an

ce

Evalu

ati

on

s of

HP

AC

O w

ith

FC

M

Val

ue/

Nei

ghb

orh

ood p

ixel

s 3x

3

5x

5

7x

7

9x

9

11x

11

Adap

tive

Thre

shold

185

164

160

148

139

Num

ber

of

Seg

men

ted

1000

1995

2285

3445

8881

Ex

ecuti

on T

ime

14

28

25

23

18

Wei

ght

14

28

25

23

28

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

163

Fig

ure

5.5

Per

form

an

ce E

va

luati

on

s of

HP

AC

O w

ith

FC

M

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

164

Tab

le 5

.2 C

om

para

tiv

e A

naly

sis

of

Exis

tin

g A

pp

roach

es

Auth

or

Typ

es o

f R

epre

senta

tion

Sli

din

g

win

dow

Val

ue

of

Wei

ght

Vec

tor

Tota

l N

o. of

Tum

or

val

ue

(pix

el)

Ex

ecuti

on

Tim

e

Ex

isti

ng A

pp

roac

h

HS

OM

- F

CM

3x

3

8

795

13.0

98

Ex

isti

ng A

pp

roac

h

GA

- F

CM

3x

3

13.6

1913

40

Pro

pose

d A

pp

roac

h

BL

OC

K B

AS

ED

TE

CH

NIQ

UE

5x

5

14

1000

14

Pro

pose

d A

pp

roac

h

HP

AC

O-

FC

M

5x

5

14

1000

14

Pro

pose

d A

pp

roac

h

PS

O-

FC

M

5x

5

14

1000

14

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

165

Fig

ure

5.6

C

om

para

tiv

e A

naly

sis

of

Exis

tin

g A

pp

roach

es



166

The comparative analysis shows that proposed system has a much lower tumor

value and lesser execution time when compared to existing approach. The following

graph shows the performance analysis of HSOM, GA, HPACO with Fuzzy. It is clear

from the graph that the tumor and the execution time are much better when compared to

existing approach.

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

167

Fig

ure

5.7

Sel

ect

th

e H

PA

CO

wit

h F

CM

Alg

orit

hm

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

168

Fig

ure

5.8

Seg

men

ted

ou

tpu

t of

HP

AC

O w

ith

FC

M A

lgori

thm

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

169

Fig

ure

5.9

Ou

tpu

t Im

ag

e sa

ved

to t

he

ou

tpu

t fo

lder

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

BR

AIN

TU

MO

R D

ET

EC

TIO

N U

SIN

G H

PA

CO

170

Fig

ure

5.1

0 C

on

firm

ati

on

of

HP

AC

O w

ith

FC

M S

aved

to O

utp

ut

Fold

er

CAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRIE

PERFORMANCE ANALYSIS

185

5.7 SUMMARY

In this work, a novel approach was applied to MRI Brain Image segmentation

based on the Hybrid Parallel Ant Colony Optimization (HPACO) with Fuzzy Algorithm

have been used to find out the optimum label that minimizes the Maximizing a Posterior

estimate to segment the image. The HPACO search is inspired by the foraging behaviour of

real ants. Each ant constructs a solution using the pheromone information accumulated by the

other ants. In each iteration, local minimum value is selected from the ants’ solution and the

pheromones are updated locally. The pheromone of the ant that generates the global

minimum is updated. At the final iteration global minimum returns the optimum label for

image segmentation. In the above 3×3, 5×5, 7×7, 9×9, 11×11 windows are analyzed the

HPACO with Fuzzy of 3×3 window is chosen based on the high contrast than 5×5, 7×7, 9×9,

and 11×11.

The detection of brain tumor region using Hybrid Parallel Ant Colony Optimization

with Fuzzy C Means is investigated. A New CAD System is developed for verification and

comparison of brain tumor detection algorithm. HPACO with FCM automatically determines

the adaptive threshold for the brain tumor segmentation.

CAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRIE

PERFORMANCE ANALYSIS

186

CHAPTER VI

CLASSIFICATION

chapter v brain tumor detection using...

Documents