a novel approach for self organisation based … · xiuming li et al.[8] states that the chan -vese...

A NOVEL APPROACH FOR SELF ORGANISATION

BASED SEGMENTATION OF MRI BRAIN IMAGES

E. SYNTHIYA JUDITH GNANASELVI1, Dr. M. MOHAMED SATHIK

2

1Research Scholar, Bharathiar University,Coimbatore, India 2Principal, Sadakathullah Appa College, Tirunelveli,India.

[email protected]

Abstract:

The detection of brain tumor in Magnetic Resonance Imaging (MRI) is very

significant but intricate task, in which different segmentation techniques get

evolved. In many of these techniques, segmentation is applied directly without any

boundary detection which takes long time, large number of iterations and yields

less accuracy result. This is a major drawback of existing segmentation

methodologies. The goal of this paper is to overcome this drawback and proposed a

novel approach for brain tumor segmentation. In this paper, there are three

instances. In the first instance, a boundary is detected from the given MRI image

and in the second instance, the segmentation techniques are applied within the

detected boundary for better accuracy in reduced time. For this purpose, SOM

method is used for predict a particular area of brain and then use geodesic, Chan

vese and region based ACM method for segmentation. In the third instance, the

morphological operation is carried out for removing the unwanted object and refills

the missing elements. The result is compared with the techniques such as geodesic,

Chan vese, region based ACM method, SOM & geodesic, SOM & Chan vese, SOM &

region based ACM method. The result shows that the proposed system works better

than existing techniques with the high accuracy, sensitivity, specificity, hammoude

distance, hausdorff distance and it has less border error and mean square error

than others.

Keywords: segmentation, Chan vese, SOM, region based ACM, Geodesic ACM.

II. Introduction:

International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 2481-2500ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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In current days, image processing is an fascinating research discipline and in

particular the medical image processing is more and more challenging field to deal

with diverse medical image types. In medical image analysis and interpretation,

automatic and exact detection and classification of tumors in brain MR images is

very significant. Tumors which are recognized and treated in the beginning time

gives preferred long haul survival over those identified of late. Prior days the tumor

was ascertained physically. Be that as it may, it will take long time and diminish

the precision. To overcome the drawback automatic brain segmentation was

implemented.

In image analysis phase, image segmentation is the initial and most decisive

task of image analysis. The objective of an image analysis is that of extracting

information from an image via image segmentation, object representation, and

feature measurement. The segmentation of medical image plays an significant task

in medical imaging applications. Segmentation can be applied in various areas of

medical field such as diagnosis, localization of pathobiology, examining anatomical

structure, treatment scheduling, and computer- incorporated surgery. However, the

unpredictability and the intricacy of the anatomical structures in the human body

have resulted a rigid setback in medical image segmentation[1].

There is lot of segmentation approaches followed. Many of these approaches

take large number of iterations, long time and thus reduce the accuracy of output

images. The goal of this paper is to overcome this drawback and proposed a novel

approach for brain tumor segmentation. The preeminent segmentation algorithm

alleviates to obtain suitable decision and endow with the finest treatment. This

paper proposes a novel approach in which SOM(Self Organising Map) method is

used for predict a particular area of brain and then use geodesic, Chan vese and

region based ACM method for accurate segmentation and classification of the brain

tumor from MR images.

Ayşe Demirhan et al. [2] developed an algorithm which is based on self-

organizing map (SOM) that is trained through unsupervised learning algorithm and

fine tuned by learning vector quantization (LVQ). In their work, they developed an

algorithm intended for clustering the SOM as an alternative of utilizing an

additional network. Input feature vector is constructed with the features acquired

from stationary wavelet transform (SWT) coefficients.

Vidhya et al. [3] offered a method which uses a SOM for segmentation that is

trained with unsupervised learning algorithm and fine-tuned with learning vector

quantization (LVQ). SOM method only considers the texture feature for

segmentation, it does not consider shape feature for increase accuracy. To resolve

this problem, the texture, shape based features are combined to classify the tumor,

edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF).

Modified Fourier descriptor is used for extract the shape feature. The segmentation

accuracy of the proposed system is high compared to the existing system. Synthiya

International Journal of Pure and Applied Mathematics Special Issue

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Judith Gnanaselvi et al [4] compared the various segmentation approaches and

concluded that SOM method is better than other methods.

Mohammed M. Abdelsameaa et al. [5] concisely reviews a few region-based

ACMs, in which supervised and unsupervised image segmentation models are

distinguished. Mainly ACMs deal with shape priors in an unsupervised way owing

to the probable lack of a specific prior information on the shape of the objects to be

segmented because a smooth boundary is preferred in segmentation. This target is

accomplished by integrating a appropriate regularization term into the energy

functional.

Phyo Thant Thant Aung et al [6] depicted a segmentation method based on

region based active contour model in which the level set approach is utilized for

retrieving the region of interest (ROI) based image compression system. The intend

of this paper is to segment an image into region of interest, other than region of

interest and background for region based medical image compression system.

A. Shenbagarajan et al [7] proposed the region based Active Contour Method

(ACM) for segmentation method which offers high accuracy, and sensitivity,

specificity measures. Van-Truong Pham et al.[11] presented a method which

implants the Geodesic Active Contour (GAC) model into the region based method

because only the edge information is considered in the original Geodesic Active

Contour model. His paper demonstrate that the proposed region aided GAC method

is much more effective than the existing, especially when dealing with images with

holes, weak edges and noises.

Xiuming Li et al.[8] states that the Chan-Vese (CV) model, fails to segment

MR images with intensity inhomogeneity. So this paper proposed a new

segmentation method which overcomes the problems in traditional methods. Robert

Crandall [9] established the Chan-Vese algorithm for image segmentation which is

efficient on a variety of images. It is especially useful in cases where an edge-based

segmentation algorithm will not adequates, because it relies on global properties

such as region areas, graylevel intensities, contour lengths to a certain extent than

local properties such as gradients. This method is very useful for noisy and blurry

images. It is very slow for some applications depending on the type and size of the

image and the number of iterations needed, the segmentation can take several

seconds.

Mohammed M. Abdelsamea et al. [16] proposed a novel ACM which combines

the advantages of the Self-Organizing Map (SOM) within the level set framework of

a new unsupervised global ACM, the Chan–Vese (C–V) model. This model is

resilient to the extra noise. It can handle the images which include objects

portrayed by complex intensity distributions.Also it yields the high accuracy results.

The remainder of the manuscript is organized as follows. In section 3, we

present a system architecture for proposed method. In section 4, we present a quick


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precis of a few strategies that already have been used for segmentation of brain

MRI images. In section 5, we proposed our novel approach for segmentation of

brain image that combines some existing methods with some new ideas which is

more vigorous than its individual components. In section 6, we present a

comparison of our consequences on a database of 25 brains, every of size

256×256×124 voxels, against segmentations generated by using experts. Finally we

conclude with a discussion of ability generalizations of our technique to phase other

systems for segmentation.

III. System Architecture:

The following system architecture is implemented in this proposed method.

Figure 1. Flow diagram of proposed method

IV. Existing Methods:

This section presents a few existing strategies that have been used for

segmentation of brain MR images.

IV.1. Geodesic ACM:

In geodesic ACM, the technique which is based on active

contours evolving in time according to intrinsic geometric measures of the image is

followed. The evolving contours naturally split and merge, allowing the

simultaneous detection of several objects and both interior and exterior boundaries.

The relation between active contours and the calculation of geodesics or minimal

distance curves is maintained. This geodesic approach for object segmentation

allows connecting classical “snakes” based on energy minimization and geometric

active contours based on the theory of curve evolution. Experimental results of this

I/P Image

Analysis

Boundary Detection Techniques

Segmentation Techniques

Preprocessing


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geodesic ACM proves that it can be used for real images including objects with holes

and medical data imagery. Also the results may be elongated to 3D object

segmentation too.[11][12] Typical variational procedures for calculating geodesic

contours generate local minima of the energy which may be sensitive to

initialization. Highly desirable anisotropic formulations tend to be slower due to

increased computational burden. [13].

IV.2. Region Based ACM:

The region based ACM for segmentation seeks to drive the curves to

reach the boundaries of the input MRI brain images. This region-based ACM use

the region based descriptors to drive the curve which provide an efficient way for

segmentation in MRI brain image analysis.

This model works based on the hypothesis that the pixel regions of the image are

statistically homogenous. It deals fit with blur images, noisy images and images

with multiple holes, disconnected regions, etc. In MRI brain image analysis the

region based active contour model since considers global properties of images such

as contour lengths and MRI image pixel regions as against local properties such as

gradients. The energy minimizing function can be represented as:

ln P(Is | p) = ∫ ∫A0 Is(x,y)dA

Where Is (x, y) is the intensity at the pixel location (x, y) in the image, and the

integral gives the total area A enclosed by the curve p. As is evident, the region-

based information visually improved the segmentation quality compared to the one

using only gradient information.[14].

IV.3. Chan vese Model:

Chan-Vese model is a prevailing and flexible method which is able to segment

many types of images, including some that would be quite difficult to segment in

means of "classical" segmentation – i.e., using thresholding or gradient based

methods The model is based on an energy minimization problem, which can be

reformulated in the level set formulation, leading to an easier way to solve the

problem [15].

V. Proposed Method:

The proposed method consists of two steps. In the first step, a

boundary is detected from the given MRI image and in the second step,

the segmentation techniques are applied within the detected boundary.

V.1. Boundary Detection:

To detect the boundary in a brain image, the following algorithm is followed.

[10]


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1. Input the MRI image in which consider image intensity value is neuron

2. Initialize the variables sigma, weight vector, winning neuron

Sigma=number of neighborhood pixels (8 or 24 or 48 or 80 or 120)

3. locate the neighborhood function using

Nf(j)=Img(j)-img(j+1)*sigma(j)

4. calculate the weight vector using

Wj(j+1)=wj+Nf(j)*img(j)-w(j)

5. compute the winning neuron using

Wn=max(wn,img(j)-w(img(j))

6. segmentation using the following:

Img(j)>=wn then

img(j)=1

Else

Img(j)=img(j)

V.2. Segmentation within the Boundary Area:

After defining the boundary, segmentation algorithms are applied within the

boundary area using the following algorithm[15].

Step 1: Give the input image.

Step 2: Create the customized boundary mask.

Step 3: Start the initial counter on the brain.

Step 4: Process the number of iterations on the infected brain in square and

elliptic form.

Step5: selecting the probable area of the brain.

Step6: Segmenting the probable area of the brain.

V.3. Refinement:

In the refinement stage, the morphological operation is performed for

eradicate the unnecessary object and replenishes the missing elements.

VI. Performance Analysis:

VI. 1. Experimental Dataset:

Experiments were carried out on a set of brain images to prove the

efficiency of the proposed scheme. For the experimental purpose, some standard 512

× 512 brain MRI images are chosen.

VI. 2. Performance Analysis:

To estimate the performance of all segmentation methods, several

performance metrics are available. This paper utilizes the performance metrics such

as Sensitivity, Accuracy, Border Error, Specificity, Hammoude Distance, Hausdorff

Distance, Mean Square Error and PSNR which were calculated using below

formulas:


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• True Positive (TP): Abnormal brain correctly identified as abnormal.

• True Negative (TN): Normal brain correctly identified as normal.

• False Positive (FP): Normal brain incorrectly identified as abnormal.

• False Negative (FN): Abnormal brain incorrectly identified as normal.

Sensitivity:

Sensitivity also called the true positive rate or the recall rate in some fields

measures the proportion of actual positives.

Sensitivity = #(TP)

#(TP)+#(FN)

Accuracy:

The accuracy can be defined as the percentage of correctly classified

instances.

Accuracy = #(TP) + #(TN)

#(TP) + #(TN) + #(FP) + #(FN)

Border Error:

It is the ratio of the area covered by the XOR of segmented result (SR)

and ground truth (GT) images to the area covered by GT image.

Border Error = (FP)+(FN)

(TP)+ (FN)

Specificity:

Specificity measures the proportion of negatives which are correctly identified

such as the percentage.

Specificity = #(TN)

#(FP)+#(TN)

Hammoude Distance

It makes a pixel by pixel comparison enclosed by the two boundaries.

Hammoude Distance = #(FP)+#(FN)

#(TN)

Hausdorff Distance

The Hausdorff distance finds the largest distance between the boundary

points.

Hausdorff distance = max{maxid(gti, SR), maxid(sri, GT)}

Mean Square Error


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The mean square error (MSE) is used to evaluate the difference between a 3D

image and the original 2D image. The MSE can be calculated by,

MSE = 1

n ∑(Yi − Yi)

2

n

i=1

where, Y is the 3D image and the Y is the original 2D image.

Peak Signal-to-Noise-Ratio

The peak signal-to-noise ratio (PSNR) is used to evaluate the quality between

the 3D image and the original 2D image. The PSNR formula is defined as follows:

PSNR = 10 × log 10255 × 255

1H × W

∑ ∑ [f(x, y) − g(x, y)]2W−1y=0

H−1x=0

dB

where H and W are the height and width of the image, respectively; and f(x,y) and

g(x,y) are the grey levels located at coordinate (x,y) of the original image and

attacked image, respectively.

VII. Results & Discussion:

We give comparison of the proposed algorithm to the other methods and

quantitative and qualitative experimental results obtained from the system in this

section. Segmentation algorithm developed in this study is compared to the well-

known algorithms like region based algorithm, geodesic algorithm, chan vese

algorithm.

Figure 2. Segmentation result of each processing step for a normal brain

MRI (a) Ground truth image.


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(b to j) Segmented image using various algorithms (b) SOM, (c)

geodesic ACM, (d) Chan Vese,

(e) Region Based ACM, (f) SOM and geodesic ACM, (g) SOM and

Chan Vese, (h) SOM and

Region Based ACM, (j) proposed method.

Figure 3. Segmentation result of each processing step for a abnormal brain MRI (a)

Ground truth image,

(b to j) Segmented image using various algorithms (b) SOM, (c) geodesic

ACM, (d) Chan Vese,

(e) Region Based ACM, (f) SOM and geodesic ACM, (g) SOM and Chan

Vese, (h) SOM and Region

Based ACM, (j) proposed method.

In figure 2 and 3, the normalized image and smoothened image is shown.

They are displayed after the pre processing step. The pre processing is carried out to

remove the unnecessary noise and increase the contrast of the image by using

Gaussian filter.

The visual result of each processing step for the normal and abnormal MRI

brain image which shows the segmented area of the brain most clearly in figure 2

and figure 3 respectively. Figure 2(a) and 3(a) shows the ground truth image used

for segmentation. The segmented images using various algorithms such as Geodesic

ACM, Chan vese and Region based ACM, SOM & Geodesic ACM, SOM & Chan vese

and SOM & Region based ACM are shown from Figure 2(b) to Figure 2(h) and

Figure 3(b) to Figure 3(h). Figure 2(i) and figure 3(i) shows the segmented image

using proposed method.


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Overall Performance

Algorithms Sensitivity Accuracy B_Error Specficity Hamm_D Haus_D MSE PSNR

SOM 38.638 91.063 79.952 97.764 10.336 4.995 0.089 58.697

Geodesic ACM 36.821 84.984 132.054 91.127 18.807 5.385 0.150 56.500

Chan vese 71.224 94.973 43.117 98.165 5.837 4.228 0.050 61.514

Region Based 51.659 92.747 63.580 98.032 8.411 4.515 0.073 59.856

SOM Geodesic

ACM 50.787 86.372 120.151 90.906 17.115 5.469 0.136 56.908

SOM Chan vese 71.009 95.031 42.561 98.271 5.765 4.166 0.050 61.599

SOM Region

Based 73.827 95.499 38.664 98.410 5.213 4.029 0.045 62.020

Proposed 75.098 95.826 35.747 98.631 4.823 4.004 0.042 62.388

Table 1. Average Performance evaluation for different segmentation algorithms for

25 MRI brain images

In Table I, performance of the developed algorithm is compared to the other

algorithms for 25 MR images of the patient. From the scores in Table I, it can be

seen that that our proposed algorithm performs better than other methods. It

clearly shows that on comparing with SOM method, the accuracy value and the

PSNR value of the proposed method increased by 4.763 and 3.691 respectively. Also

the MSE value of the proposed method decreased by 0.047. On comparing with

Geodesic ACM, the proposed method increased with the accuracy value of 10.842,

the PSNR value of 5.888 and also it is decreased with the MSE value of 0.108. Like

wise on comparing with Chan vese method, the accuracy value of the proposed

method increased by 0.853, the PSNR value increased by 0.874 and the MSE value

decreased by 0.008. Also on contrasing with region based ACM, the proposed

method got the increased accuracy value and PSNR value by 3.079, 2.532


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correspondingly and it is reduced by 0.031. It obviously demonstrates that on

contrasting with SOM & geodesic ACM technique, the proposed method increased

with the accuracy value of 9.454, the PSNR value of 5.48 and also it is diminished

with the MSE value of 0.094. It absolutely shows that on comparing with SOM &

Chan vese method, the accuracy of the proposed method increased by 0.795, the

PSNR value increased by 0.789 and the MSE value reduced by 0.008. Also it

undoubtedly proves that the proposed method acquired the increased accuracy

value of 0.327, PSNR value of 0.368 and the reduced value of 0.003 on comparing

with SOM & region based method.

Sensitivity Performance

Sampl

e

Image

SOM Geo_

ACM

Chan

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image

1

37.24

8

51.99

0

74.20

2

80.49

1 52.039 75.135 79.951 83.047

Image

2

35.01

8

55.40

5

74.20

7

70.32

9 47.709 74.148 76.146 76.322

Image

3

45.50

9

49.07

8

76.97

8

71.86

2 50.089 77.811 79.120 80.845

Image

4

52.08

9

35.04

2

73.03

6

39.88

9 60.501 71.421 74.373 76.323

Image

5

51.16

8

36.37

4

71.02

3

39.87

8 55.895 69.577 73.582 75.362

Table 2. Sensitivity evaluation for segmentation algorithms

Accuracy Performance

Sampl

e

Image

SOM Geo_

ACM

Cha

n

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image

1

90.6

9

85.4

6

95.5

4 96.51 85.24 95.77 96.37 97.08

Image

2

90.7

5

88.6

2

95.4

7 94.97 86.76 95.51 95.77 95.92

Image

3

91.0

8

87.8

5

95.8

5 95.24 87.62 96.04 96.45 96.77

Image

4

92.4

1

86.5

1

95.4

3 91.90 89.31 95.34 95.84 96.22

Image

5

92.4

4

86.6

9

95.1

3 91.90 88.80 95.07 95.64 96.08

Table 3. Accuracy evaluation for segmentation algorithms


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Border Error Performance

Sampl

e

Image

SOM Geo_

ACM

Chan

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image

1

74.93

9

117.10

1

35.92

1

28.10

8 118.870 34.054 29.189 23.538

Image

2

89.07

2

109.51

8

43.59

6

48.41

4 127.438 43.243 40.717 39.248

Image

3

86.97

2

118.38

2

40.45

2

46.40

1 120.643 38.549 34.622 31.469

Image

4

69.24

8

123.12

0

41.67

1

73.92

8 97.605 42.563 37.939 34.485

Image

5

68.91

0

121.24

6

44.38

3

73.80

4 102.058 44.883 39.711 35.762

Table 4. Border Error evaluation for segmentation algorithms

Specificity Performance

Sampl

e

Image

SOM Geo_

ACM

Chan

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image

1

98.27

2

90.20

1

98.56

4

98.78

0 89.944 98.697 98.704 99.066

Image

2

97.20

8

92.47

4

97.93

6

97.82

7 91.289 97.984 98.045 98.195

Image

3

96.28

7

92.28

7

98.00

7

97.91

2 91.913 98.130 98.429 98.592

Image

4

97.37

5

92.84

4

98.19

0

98.30

0 92.851 98.280 98.485 98.670

Image

5

97.52

5

92.89

7

98.10

1

98.31

4 92.856 98.218 98.361 98.629

Table 5. Specificity evaluation for segmentation algorithms

Hammoude Distance Performance

Sampl

e

Image

SOM Geo_

ACM

Cha

n

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image 10.81 18.41 5.16 4.036 18.743 4.893 4.194 3.370


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1 5 2 9

Image

2

10.62

2

13.72

9

5.16

0 5.737 16.183 5.116 4.814 4.633

Image

3

10.32

7

14.66

6

4.71

9 5.418 15.007 4.491 4.022 3.649

Image

4 8.750

16.31

6

5.22

2 9.253 12.934 5.329 4.740 4.300

Image

5 8.710

16.08

9

5.57

7 9.254 13.548 5.633 4.977 4.470

Table 6. Hammoude Distance evaluation for segmentation algorithms

Hausdorff Distance Performance

Sample

Image SOM

Geo_

ACM

Cha

n

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image1 5.09

9

5.38

5

4.00

0 3.606 5.568 3.873 3.742 3.606

Image2 5.19

6

5.29

2

4.79

6 4.359 5.292 4.690 4.359 4.123

Image3 5.09

9

5.29

2

4.24

3 4.359 5.745 3.873 3.873 3.606

Image4 5.09

9

5.09

9

4.12

3 4.690 5.477 4.123 4.000 4.000

Image5 5.19

6

5.38

5

4.24

3 4.899 5.477 4.472 4.359 4.123

Table 7. Hausdorff Distance evaluation for segmentation algorithms

MSE Performance

Sample

Image SOM

Geo_

ACM

Cha

n

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image1 0.09

3

0.14

5

0.04

5 0.035 0.148 0.042 0.036 0.029

Image2 0.09

3

0.11

4

0.04

5 0.050 0.132 0.045 0.042 0.041

Image3 0.08

9

0.12

1

0.04

2 0.048 0.124 0.040 0.036 0.032

Image4 0.07

6

0.13

5

0.04

6 0.081 0.107 0.047 0.042 0.038


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Image5 0.07

6

0.13

3

0.04

9 0.081 0.112 0.049 0.044 0.039

Table 2. MSE evaluation for segmentation algorithms

PSNR Performance

Sampl

e

Image

SOM Geo_

ACM

Chan

vese

Regio

n

SOM

_Geo_AC

M

SOM_Cha

n vese

SOM

_Regio

n

Propose

d

Image

1

58.44

2

56.50

4

61.63

6

62.70

1 56.439 61.868 62.537 63.472

Image

2

58.46

8

57.57

1

61.57

1

61.11

6 56.912 61.606 61.868 62.027

Image

3

58.62

6

57.28

7

61.95

0

61.35

4 57.204 62.159 62.626 63.040

Image

4

59.33

0

56.83

1

61.53

6

59.04

6 57.840 61.444 61.944 62.358

Image

5

59.34

4

56.89

0

61.25

5

59.04

6 57.639 61.206 61.738 62.193

Table 2. PSNR evaluation for segmentation algorithms

The table 2 shows the sensitivity evaluation of 5 MRI brain images. In table

2, for example we take image 3 which shows the sensitivity difference of 30.756 in

SOM & geodesic ACM, 3.034 in SOM & chan vese and 1.725 in SOM & region based

ACM on compared with the proposed method. The other images are also shows the

sensitivity difference similar to image3. The table 3 illustrates the accuracy

estimation of 5 MRI brain images. In table 3, for instance the image 3 which shows

the accuracy difference of 9.15 in SOM & geodesic ACM, 0.73 in SOM & chan vese

and 0.32 in SOM & region based ACM on compared with the proposed method. The

other images are also shows the accuracy difference similar to image3.

The border error rate evaluation and MSE evaluation is shown in table 4 and

table 8 respectively. For example an image 3 is chosen for evaluation of both

performance metrices. The border error rate value and MSE value decreased by

89.174, 5.836 respectively in SOM & geodesic ACM, 7.08, 0.881 respectively in

SOM & chain vase and 3.153, 0.414 in SOM & region based ACM on comparing

with the proposed method. The other image is also shown like this. The table 5

displays the specificity analysis. It obviously demonstrates that in image 3, the

proposed method offes the increased specificity vaue of 0.679 than SOM & geodesic

ACM technique, 0.462 than SOM & chan vese and 0.163 than SOM & region based

ACM. The other images are also displays the specificity difference on comparing

with the proposed method.

The table 6 and table 7 illustrates the evaluation of Hammoude distance and

Hausdorff distance. We choose image 3 for example. For both the Hammoude

distance and Hausdorff distance evaluation, the proposed method decreased by the


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value of 11.358, 0.753 respectively than SOM & geodesic ACM, 0.842, 2.139 than

SOM & chan vese and 0.373, 0.267 than SOM & region based ACM.

The table 8 demonstrates the PSNR value analysis of 5 MRI brain images.

For instance an image 3 is chosen for analysis. It clearly shows that on comparing

with SOM & geodesic ACM, SOM & chan vese and SOM & region based ACM , the

PSNR value of the proposed method increased by 5.836, 0.881 and 0.414

respectively.


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Figure 4: Graphs showing the performance analysis of segmentation with respect to

measurement parameters for all the 20 MRI brain images.

Conclusion:

Segmentation of MRI data is a challenging and time consumption task.

Segmentation techniques must afford efficient and accurate results for the purpose

of analysis and diagnoising the abnormalities present in the medical image.

Generally, boundaries of different tissues in MRI brain images are not clear because

white and gray matter intensities were very close which makes the boundary

identification to be difficult. We have proposed a method that can overcome the

difficulty of accuracy and diagnosis time and results with increased yield. By

comparing the results, our proposed method has higher performance and it offer

better accuracy (atleast 10% higher) and least Mean Square Error in comparison to

other models. Hence, the proposed method yields the better segmentation results

than the existing methods.

References:


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a novel approach for self organisation based … · xiuming li et al.[8] states that the chan -vese...

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