FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHM

International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
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FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE
USING LEVEL SET ALGORITHM
Ismail Yqub. Maolooda Songfeng Lub* Yahya E. A. Al-Salhic Shayem AL resheedid Mahmut Incee
a,b*,d,eSchool of Computer Applied and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd,
HongshanQu, Wuhan Shi, Hubei Sheng430073, China.
c General Directorate of Education DhiQar, Ministry of Education, 64001,Iraq.
*Correspondence Author: lusongfeng@hust.edu.cn;
Manuscript History
Number: IJIRIS/RS/Vol.05/Issue05/JYIS10080
DOI: 10.26562/IJIRAE.2018.JYIS10080
Received: 10, July 2018
Final Correction: 18, July 2018
Final Accepted: 21, July 2018
Published: July 2018
Citation: Maolood, Lu, Al-Salhi, resheedi & Ince (2018). Fuzzy Segmentation of MRI Cerebral Tissue Using Level set
Alogrithm. IJIRIS:: International Journal of Innovative Research in Information Security, Volume V, 25-35.
doi://10.26562/IJIRIS.2018.JYIS10080
Editor: Dr.A.Arul L.S, Chief Editor, IJIRIS, AM Publications, India
Copyright: ©2018 This is an open access article distributed under the terms of the Creative Commons Attribution
License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited
Abstract—The current study investigated a median filter with the fuzzy level set method to propose fuzzy
segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input
image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image
clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then
used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive
to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal
solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm
efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in
previous studies.
Keywords—Image segmentation; Fuzzy clustering; Level set method;
I. INTRODUCTION
Image segmentation is an essential technique for processing MRI cerebral tissue images. Image segmentation is the
separation of a digital image into numerous segments or pixel sets, in which pixels are grouped similarly according
to homogeneous criteria, such as texture, intensity, or color, to discover items and borders inside the defined image
[1]. Many algorithms, such as graph cut, level set, edge detection, and clustering, have been proposed for medical
image segmentation [2]. Medical image segmentation aims to divide images into homogeneous partitions that
concern other pixel neighborhoods to make these images significant in realizing the aims of medical images. The
results of image segmentation are processed by extracting a set of segments, regions, or contours of the image.
Pixels in a region possess several similar characteristics or computed properties, such as contrast, texture, color,
and grayscale [3]. Fuzzy c-means (FCM) is an important algorithm used in MRI cerebral tissue segmentation [4, 5].
FCM is successfully improving the performance of medical image segmentation [5]. Jiang et al. [2] are introduced
and proposed the current scheme with spatial constraints LCFCM_s algorithm for spatial information to the FCM
algorithm which pixel enables to affect by its immediate neighborhood. This method entails increased time
consumption because of the calculation in each iteration step to the spatial neighborhood [2].

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First, local correntropy based FCM clustering with spatial constraint LCFCM_s is introduced. Second, it is simplified
to a new corresponding robust LCFCM_s1 for image segmentation by proposing a novel level set algorithm based on
clustering that supplies sufficient robustness and noise. Benchara proposed a distributed algorithm based on fuzzy
c-means clustering (DFCM) to implement and analyze MRI medical images [6]. This method defines initial and final
cluster centers for medical image segmentation. Many filters, such as high pass, hybrid, linear, median, and low pass,
have been used to remove noise from medical images [7–11]. The median filter is essential in removing noise in
medical images. Median filtering is a nonlinear circuit for reducing and removing certain types of noise from
medical images. Median filters are frequently used in image and signal processing, communication, peak detection,
robotic vision, speech processing, and medical imaging [9–13]. Sethian and Osher proposed the level set algorithm
with intensity in homogeneity and noise; it was the first algorithm used in medical image segmentation [14]. Li et al.
and Wang proposed an active region according to the level set algorithm by introducing a kernel function with local
binary fitting (LBF) energy, which is a model of LBF that regards spatially different native medical image
information as constraints [15, 16]. In this study, we developed the median filter with fuzzy level set (MFFLs)
algorithm for MRI cerebral tissue segmentation by introducing median-filter-based fuzzy c-means clustering with
the level set algorithm. The developed algorithm was applied to 80 MRI brain images to validate its effectiveness.
The algorithm was then utilized to remove noise to clear an image, create image clusters, and reveal initial and final
cluster centers. After image segmentation, white matter was extracted and separated from grey matter.
II. RELATED WORKS
Image segmentation is the partitioning of an image into several disjoint areas, particularly images with similar
characteristics, such as color, texture, and intensity. Image segmentation is used in many areas, such as cellular
network architecture, color texturing based on an image segmentation system, cellular network and medical
segmentation [17]. Medical image fragmentation aims to identify atomic structures, such as cysts, kidney tumors,
brain tumors and breast cancer, from images with interesting abnormality [18]. Clustering is a classification process
in which areas are sectored in such a manner that cluster specimens with similar characteristics are clustered
together and apart from samples belonging to another cluster [19]. Hard and fuzzy clustering are the two primary
clustering strategies. Gaussian noise is embedded in medical images, and Gaussian distribution involves groups of
values obtained from a zero mean that is added to each value. Impulsive noise involves replacing certain pixels with
random values. The noise problem is solved by modifying fuzzy c-means clustering to create filtering sigma theory
for computing a separate MRI brain image of neighboring pixels, leading to the improvement of image quality
during segmentation [20]. Seyedarabi and Shamsi modified the partial FCM algorithm by using two factors in this
method [21]. The first one considers the distance between the central pixel and the neighbor pixels, and the second
differentiates the values of central and neighboring pixels. Compared with traditional fuzzy c-means clustering for
images, the suggested method exerted more effect on image segmentation noise and resulted in less noise in the
image. However, the complexity of the brain makes the results of this method imprecise [22]. In 1988, Sethian and
Osher proposed the level set algorithm as a means of using numerical methods in path contour development [14]. A
new size of data input was produced by the new technique for solving partial differential equations (PDEs) with
function expression extraction. Segmentation accuracy is solved by the implementation of this new technique by
developing a numerical approximate for the level set algorithm, thus explaining area boundaries with curvature
based on speeds and regularizing solutions by regarding a medical image as a group of continuous functions.
Extensive progress has been achieved due to a particular level idea method, and the development and
implementation of methods have been improved [23, 24].
III.PROPOSED METHOD
In this study, the median filter with fuzzy level set (MFFLs) method was developed for segmenting MRI cerebral
tissue images. For this purpose, three algorithms (A, B, and C) were proposed. An MRI image was used as an input
medical image, and the algorithms were applied to remove noises using a median filter. FCM clustering was
performed to create a cluster tissue by separating white matter from gray matter with the use of the level set. FCM
works by assigning membership to all data pixels that have a point that belongs to the cluster center on the basis of
the distance between the data pixels and the cluster center. Certain data pixels are closer to the center than others,
and their membership is toward the particular cluster center. The membership of all data pixels must be equal to 1.
The cluster centers and every iteration membership are updated as follows.
1. NOISE REMOVABLE
The MRI technique confirms that removing noises from MRI images is difficult. The expected results are challenging
to analyze because of their grayscale, and the intensity among the pixels is minimally modified. Different techniques
have been used to reduce noises in medical images. One of the most important methods is the median filter [25].

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In this method, the filter determines a median value from a group of values that have been sorted in ascending order.
The median group always comprises the values and window size on odd numbers. The size of the median window
which also called the mask has fixed sizes 3 x 3, 5 x 5 and 7 x 7 [26]. The median filter is a filter based a statistical
sort, the principle is that pixels in the genuine image at the point(I, J)The sorting and counting all the values in the
neighboring of (I, J) and will be in the middle of the values matrix as (I, J)of the 8th neighboring of values were: 101,
69, 0, 56, 255, 87, 123, 96, 157, Statistics sort: 0, 56, 69, 87, 96, 101, 123, 157, 255. The middle point in the matrix is
96 that are (I, J) value point median filter method response [26].The median filter algorithm could be summarized
as follows.
Algorithm A: Noise Removal Algorithm.
Input: MRI image.
Output: Filtered image.
Step 1. Read the MRI cerebral tissues image.
Step 2.Partition the original image into blocks of 3 x 3 pixels.
Step 2.1. Sort the values of the pixel in ascending order.
Step 2.2. Choose the middle value.
Step 2.3. Change the target pixel with the middle value.
Step 3. Repeat Step2 until the process is completed for the entire image.
Step 4. End
II. CLUSTERING ALGORITHM
Data clustering is a statistical method for analyzing data in many fields, such as image clustering, pattern
recognition, image analysis, data mining, bioinformatics, and machine learning [27]. The FCM algorithm can be used
to create grouping by partitioning data points into groups with the most significant similarity in data objective, with
the maximum or minimum similarity of data points among different groups [28].
2.1 FUZZY C-MEANS
FCM clustering is one of the essential methods that can be used to create clustering for a medical image. FCM is
widely used in image segmentation [29]. The standard FCM algorithm provides a partition of medical images. The
FCM technique is utilized to create the cluster. Each pixel of a dataset corresponds to the distance between the
cluster center and a data point. This algorithm is frequently used in pattern recognition and was developed in 1973
by Dunn and improved in 1983 by Bezdek [30]. The FCM algorithm provides a segmentation of medical images.
Tissue classification, including the quantification of volume tissues, the discovery of pathology, and computer-
integrated surgery, is a necessary step in medical imaging implementation. FCM partitions a set of n objects
X = {x , x , … , x } in dimensional space, where c is (1 < c < n) fuzzy clusters with C = {c ,c , … , c } cluster centers
or centroids. The fuzzy clustering objects are given in Eq. (1) by n rows and c columns with fuzzy matrix µ, in which
c is the number of clusters and n is a number of data objects. μ is the value in the ith row and jth column in μ that
indicates the degree of the membership function of the ith object with the jh cluster [27, 31].
J = μ d (1)
where ( > 1) is any real number, is the row and number of data objects, is the columns and the number of
clusters, is the degree of the membership function of the th object with the th cluster, and the is given in Eq.
(5). The minimization of the following objective function and the characteristics of must satisfy the following
three conditions.
 The range of membership value between 0 and 1 is given in Eq. (2) as follows:
∈ [0,1] , ∀ = 1,2, … , , ∀ = 1,2, … , (2)
 The summation value of the membership for each data point being equal to 1 is given in Eq. (3).
= 1 ∀ = 1,2, … , (3)
 The summation value of all membership in the cluster being smaller than the number of data objects is
given in Eq. (4).
0 < < 1 ∀ = 1,2, … ,
(4)
= − (5)

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where is the norm ||*|| for any standard expressing the similarity between the center and any measured data.
is the th d-dimension center of the cluster, and is the th of d-dimensional measured data. is greater than 1,
and is a scalar that describes the weighting exponent and controls the fuzziness of the resulting clusters. is the
Euclidian distance from object to cluster center , and is the centroid of the th cluster [31, 32].
=
∑
∑
(6)
=
1
∑
(7)
This method works by allocating membership to every data value corresponding to every cluster center on the
basis of the distance between the data value and the cluster center. The data value that is near the cluster center
has the most membership toward the particular cluster center. Clearly, the collection of membership of every data
value must be equal to 1. Afterward, cluster centers and every iteration membership are updated according to Eq.
(7) [32, 33]. The FCM algorithm can be summarized as follows.
Algorithm B: Fuzzy C-mean (FCM) Algorithm
Input: Filtered image.
Output: MRI image with initial boundaries of cerebral tissues.
Step 1.Read MRI cerebral tissue image.
Step2.Define cluster numbers to be equal to , where < ≤ 3.
Step3. Select where is greater than 1. The values of the membership function are initializing , =
, , … , ; = , , … , .
Step4.Computing the cluster center , = , , … , , according to Eq. (6).
Step5.Computing Euclidian distance , = 1, 2, … , ; = 1, 2, … , , according Eq. (5).
Step 6. The membership function is updating where , = , , … , ; = , , … , according to Eq. (7).
Step 7. If converged, addition by 1 where = + and go back to step 2.
Step 8. End.
2.2 SEGMENTATION WITH THE LEVEL SET METHOD
The level set algorithm for segmenting a cerebral tissue image is implemented. The medical image shapes are
grayscale. Assume that = ( , ) is the medical image, where ∈ [1, ]and ∈ [1, ], while is the number of
image pixels. = ( , )is the point of the medical image in the front and develops over time, such that ( ) is the
position over time. Each point ( ) of time is on the highest surface of Eq. (8).
∅ ( ( ), ) = 0 (8)
This method depends on a PDE function ∅ ( , , ) [30, 34], and evaluation is possible by approximating the active
contours by tracking zero level set ( ), as shown in Figure 1, which explains Eq. (9).
( ) =
∅( , , ) < 0, ( , ) ( )
∅( , , ) = 0, ( , ) ( )
∅( , , ) > 0, ( , ) ( )
(9)
Fig.1. Explain of Level set function.

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The development of a curved surface is characterized by the various forces of the internal and external research
archives. Surface t leads to the highest setting that is equal to the distance from the nearest pixel on an active
contour to ( , ) , such that ∅ ( , , , ) < 0, ( , ) with distance ( ) being negative inside the contour.
( , )with ( )is positive outside the contour, and ( )is equal to zero. The initial function ∅ of the level set
matches the initial contour. The initial function ∅ at = 0 must be possible to initialize function ∅ at each time
with the equation
∅
∅
and the chain rule [35].
∅( ( ), )
∅
= 0
∅
( )∅
( )
+
∅
= 0
∅
( )
+ ∅ = 0
(10)
In particular, the development of initial function ∅ is completely determined by the numerical level set equation.
∂∅
∂t
+ f|∇∅| = 0
∅(0, x, y) = ∅ (x, y)
(11)
where |∇∅| shows the natural direction, ∅ (X, Y), and F represents the initial contour of the casing, including the
power engineering internal interface (e.g., the mean curvature along the contour and area) and gradient synthetic
image of artificial momentum by external forces [34, 36]. The progress requiresF to be regularized by an edge
indicator function g in order to stop level set evolution near the optimal solution.
g =
1
1 + |∇(Gσ ∗ I)|
(12)
whereGσ ∗ I stands for the convolution of medical image I with Gaussian noise Gσ, ∇ denotes the operation for the
medical image gradient, and the function g is around zero in variation boundaries [19]. A popular formulation for
level set segmentation is
∂∅
∂∅
= |∇∅| div
∇∅
|∇∅|
(13)
Below the clustered image has been segmented using the fuzzy level set segmentation (FLSS) algorithm to achieve
MRI cerebral tissue segmentation of the image. The required steps have been followed.
Algorithm C: Level Set Algorithm
Input: MRI image with initial boundaries of cerebral tissues.
Output: MRI cerebral tissue segmentation.
Step1. Create a loop for reading the first cluster of MRI image with initial boundaries of cerebral tissues.
Step2.If∅ (t, x, y) > 0 then go inside the cluster image.
Else if∅ (t, x, y) = 0 then go the boundary of the cluster image.
Else∅ (t, x, y) < 0 go the outside the cluster image, according Eq. (9).
Step3.Initializefunction ∅ of the level set matches the initial contour with chain rule by Eq. (10).
Step4. Evaluate the function ∅ is entirely by the numerical level set by using Eq. (11).
Step5. If the function g is near 0 in a boundary, go to Eq. (12), and go to the below formula to create the MRI
cerebral tissue segmentation by Eq. (13).
Step6.If there is remaining clusters return back to step1 otherwise finish the loop.
Step7. End.
IV. EXPERIMENT RESULT AND DISCUSSION
Performance evaluation and experiments are implemented on cerebral tissue image segmentation for medical
images, including an MRI image of the human brain. The images contain a variety of cerebral diseases, which exist in
different shapes, sizes, and locations in the brain. The dataset is utilized to evaluate the method, and the output of
each step is presented and discussed. Afterward, a comparison is carried out with recently related methods in the
literature. Our proposed method requires experiments for MRI brain image analysis. In this process, we select MRI
cerebral tissue images, as shown in Figure 2, and compare the performance of the proposed method with that of
using the median filter in terms of removing the impact of Gaussian noise in each MRI image. Given that this method
has the highest computational complexity and is time-consuming, the technique of searching window to window is
used in the comparison. FCM is applied to create cerebral tissue clusters for the human brain, as shown in Figure 3.

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Fig.2.(a, b and c) are original MRI medical image, and (d, e and f) are filtered image.
This algorithm is determined by assigning membership to every data image value corresponding to the center of
every cluster on the basis of the distance between the cluster center and data point. The proposed method is applied
on parts of white and gray matter in the initial and final cluster centers of Cases 1 to 3. A detailed comparison is
performed between DFCM [6] and the proposed MFFLs for each part of white matter (WM) and gray matter (GM) in
the brain images according to the initial or final cluster center. Our method provides good valuations for the cluster
centers and demonstrates better practical convergence to the final cluster centers than DFCM [6].The values of the
initial and final cluster centers in DFCM are lower than those in the proposed method, as shown in Tables 1 and 2
with Figures 4and5.
Fig.3.The results of MRI medical images (a,b and c) are cluster images for gray matter, and (d, e and f) are cluster
images for white matter.
TABLE 1. DFCM AND OUR METHOD COMPARISON BETWEEN FINAL CLUSTER CENTER AND INITIAL CLUSTER
CENTER BASED ON A NUMBER OF ITERATIONS FOR WHITE MATTER.
Comparison Method Initial Cluster Center Final Cluster Center Number of
IterationsCase1 Case2 Case3 Case1 Case2 Case3
DFCM 1.1 2.5 3.8 1.100 97.667 146.569 13
MFFLs 3.7634 4.1807 5.6449 102.5521 103.4951 163.4565 15

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TABLE 2.DFCM AND OUR METHOD COMPARISON BETWEEN FINAL CLUSTER CENTER AND INITIAL CLUSTER
CENTER BASED ON A NUMBER OF ITERATIONS FOR GRAY MATTER.
(a) (b)
Fig.4. DFCM and our method comparison between final and initial cluster center based on number of iterations: (a)
initial cluster center for white matter, (b) final cluster center for white matter.
(a) (b)
Fig. 5. DFCM and the current proposed method comparison between final and initial cluster center based on
number of iterations: (a) initial cluster center for gray matter, (b) final cluster center for gray matter.
The implementation of a fuzzy level set algorithm to apply a part of WM and GM of the human brain utilizing
dynamic variation limits. In this experiment the biggest number of iterations is 15, where the iteration number
affects the output quality of an image; however, the Implementation time is increased. It is appropriate to unify the
forces for medical image segmentation. This study explains several cases of MRI cerebral tissue segmentation for
white matter and gray matter. The fuzzy level set formulation is configured as α = 0.5, β = 0.6, λ1 = 0.2 and λ2= 0.4
are utilized, and the amount of reprimand imposed on the integrals outside and inside the contour is different. In
addition, the equality of λ1 and λ2 demonstrate fair competition outside and inside the boundary during the
evolution.
Comparison Method Initial Cluster Center Final Cluster Center Number of
IterationsCase1 Case2 Case3 Case1 Case2 Case3
DFCM 1.1 2.5 3.8 1.100 97.667 146.569 13
MFFLs 2.0062 3.4427 5.1138 88.7257 99.3391 159.8158 15

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The both of alpha (α) and beta (β) which is used in this experimental are increasing alpha and decreasing value of
beta correspond to the dependency of a location of the initial contour while decreasing values corresponds to a
more accurate location of the object boundaries. By using a correntropy standard, MFFLs can successfully extract
the desired objects, in spite of the presence and weak boundaries extreme noise. Figures 6 and 7 in the rows (a, b)
show the final evolution of a fuzzy level set of white matter and gray matter segmentation, at the end of the process
as shown in Figures 6 and 7 in the row (c) to the extracted region of final segmentation.
Fig. 6.MRI cerebral tissue segmentation based on fuzzy level set for white matter: row (a) stared segmentation, row
(b) final segmentation after 3, 9 and 15 iterations, respectively, with α = 0.5, β = 0.6, λ1 = 0.2 and λ2= 0.4 and
row(c) extracted region of final segmentation.
Fig.7. MRI cerebral tissue segmentation based on fuzzy level set for gray matter: row (a) stared segmentation, row
(b) final segmentation after 3, 9, and 15 iterations, respectively, with α = 0.5, β = 0.6, λ1 = 0.2 and λ2= 0.4 and
row(c) extracted region of final segmentation.
The results of Figures 6 and 7 were quantitative comparison the accuracy of that MRI cerebral tissue segmentation
the part of white and gray matters were given in Tables3 and 4 with Figure 8. It discovers that our MFFLs
segmentation algorithm achieves not only the best accuracy in all three cases of MRI images but also the highest
robustness to noise. This experiment explains again the proposed algorithm that had a better ability to resist the
impact of noise in medical images. The size of medical image patches is an essential parameter in our median filter
with the fuzzy level set segmentation algorithm. It determines how benefit medical image information will be used
and the limitation of spatial smoothness.

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TABLE 3.A NUMBER OF ITERATIONS AND CLUSTER CENTER OF THE PROPOSED ALGORITHM FOR WHITE
MATTER
Medical Image Initial Cluster Center Final Cluster Center Number of Iteration Time Steps
Case1 2.0064 88.7257 3 8.447
Case2 3.4427 99.3391 9 2.21
Case3 5.1138 159.8158 15 5.912
TABLE 4.A NUMBER OF ITERATIONS AND CLUSTER CENTER OF THE PROPOSED ALGORITHM FOR GRAY MATTER
Medical Image Initial Cluster Center Final Cluster Center Number of Iteration Time Steps
Case1 3.7634 102.5521 3 12.15
Case2 4.1807 103.4951 9 11.3
Case3 5.6449 163.4565 15 8.584
We evaluate the performance of MFFLs algorithm, then the comparison among algorithms such as LBF, LGDF, LCK,
LCFCM_S, LCFCM_S1, and MFFLs for segmentation of medical images [2]. The study successfully applied a proposed
method of MFFLs to record for optimizing the performance of the similar premise then edge clear for medical image
segmentation and better than another algorithm as shown in Figures 6 and 7 in rows a and b are presented in
Tables 5.
TABLE 5. MFFLS METHOD AND ANOTHER METHODS COMPARISON WITH A NUMBER OF ITERATIONS AND
COMPUTATIONAL TIME (S) FOR WHITE MATTER AND GRAY MATTER IMAGES
Comparison Method
White Matter Cluster Image Gray Matter Cluster Image
Case1 Case2 Case3 Case1 Case2 Case3
LBF
Iteration 180 200 300 180 200 300
Time 31.51 5.2 21.79 31.51 5.2 21.79
LGDF
Iteration 1250 300 240 1250 300 240
Time 99.52 12.47 9.33 99.52 12.47 9.33
LCK
Iteration 300 580 260 300 580 260
Time 43.73 21.16 78.87 43.73 21.16 78.87
LCFCM_S
Iteration 170 360 160 170 360 160
Time 33.75 21.06 54.25 33.75 21.06 54.25
LCFCM_S1
Iteration 170 360 160 170 360 160
Time 22.31 10.66 15.08 22.31 10.66 15.08
MFFLs
Iteration 3 9 15 3 9 15
Time 8.447 2.21 5.912 12.15 11.3 8.584
V. CONCLUSION
Treating image noise and homogeneities while retaining edges and feature detail requires the selection of the
correct position of the initial cluster as the FCM is sensitive and needs to be dealt with accurately because incorrect
calculation causes the algorithm to stick at sub-optimal solutions. FCM works in the search area and must be moved
from one point to another until it reaches its final destination peak. We proposed median filter with fuzzy level set
algorithm is presented for fuzzy segmentation of MRI cerebral tissue images. Our proposed algorithms have been
applied for 80 MRI medical images in order to validate the efficiency method. A results of our MFFLs method
showed that the clustering to optimize the performance of the same premise, then edge clear of image segmentation
and better than DFCM especially for initial cluster center.
ACKNOWLEDGMENT
The Authors would like to thank Huazhong University of Science and Technology (China), EdithCowan University
(Australia), Chinese Scholarship Council, and the Science and Technology Program of Shenzhen of China under
Grant Nos. JCYJ20170307160458368 and JCYJ20170818160208570
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