International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
A Review on Brain Tumor Segmentation Techniques for
MRI Images
Jinal A. Shah#1, S. R. Suralkar*2,
#1ME Final year student *2 HOD of Electronics & Telecommunication dept.,,SSBTs COET
Bambhori,Jalgaon (India)
Abstract— Increase in fatal rates because of
the Brain tumor is an alarm for an efficient Image
segmentation. This has become one of the crucial
tasks in medical image analysis and is often the first
and the most critical step in a good deal of clinical
diagnosis. In brain MRI analysis, image segmentation
is commonly used for measuring and visualizing the
brain’s anatomical structures, for analyzing brain
abnormalities and the extent of tumor, for delineating
pathological sections, and for surgical planning and
image-guided interventions. Bearing this in mind, an
automatic segmentation of brain MR images is needed
to correctly segment White Matter (WM), Gray Matter
(GM) and Cerebrospinal Fluid (CSF) tissues of brain
in a shorter span of time. The accurate segmentation
is essential, otherwise the wrong identification of
disease can lead to severe consequences. Till date,
various segmentation techniques of different accuracy
and degree of complexity have been developed and
reported in the literature.In this paper, we review the
most popular methods commonly used for brain MRI
segmentation. Taking into account the aforementioned
challenges, we first introduce the basic concepts of
image segmentation followed by some conventional
methods in brief. Comparison of Segmentation method
table which
focuses towards highlighting the
strengths and limitations of the earlier proposed
segmentation techniques discussed in the literature.
Besides summarizing the literature, a critical
evaluation of the surveyed literature is also provided
which reveals new facets of research. However,
articulating a new technique is beyond the scope of
this paper.
Keywords—Brain Tumor Detection, Medical
Image Processing, Segmentation, MRI, Fuzzy C
Means Clustering
I INTRODUCTION
Over the last few decades, the rapid development of
noninvasive brain imaging technologies has given a
new wings in analyzing and studying the brain
anatomy and function. Enormous progress in
accessing injury in brain and exploring brain anatomy
has been made using magnetic resonance imaging
(MRI). The advances in brain MR imaging have also
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provided large amount of data availing an increasingly
high level of quality. The analysis of these large and
complex MRI datasets has tedious and complex task
for clinicians, who use manual method to extract
important information. This manual analysis is usually
time-consuming and prone to errors due to various
inter- or intra operator variability studies. These
complexities in brain MRI data analysis required
inventions in computer-aided methods to improve
disease diagnosis and testing. These days,
computerized methods for MR image segmentation,
registration, and visualization have been widely used
to assist doctors in qualitative diagnosis. [24][21][20]
Brain MRI segmentation is an essential task
in many clinical applications as it influences the
outcome of the entire analysis.For example, MRI
segmentation is commonly used for measuring and
visualizing various brain structures, for delineating
lesions, for analysing brain development, and for
image-guided interventions and surgical planning.
This diversity in image processing applications has led
to development of various segmentation methods of
different accuracy and degree of complexity. [24][23]
Image Segmentation is an image processing
process which aims to partition an image into
homogeneous regions composed of pixels with the
same characteristics according to predefined criteria.
Many methods of image segmentation requires the
adjustment of several control parameters to obtain
good results.
The purpose of image segmentationis to
divide an image into a set of semantically
meaningful,homogeneous,
and
non-overlapping
regions of similarattributes such as intensity, depth,
color, or texture. Thesegmentation result is either an
image of labels identifyingeach homogeneous region
or a set of contours which describethe region
boundaries. [24]
Most of the work available by the scholars
has focused on 2D images. To segment the 3D image
which is generally obtained from a series of MRI
images, each image ―slice‖ is segmented individually
in a ―slice-by-slice‖ manner. Method of slicing
always need post processing to connect segmented 2D
slices into 3D volume. Still, the resultingsegmentation
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can contain inconsistencies and non-smooth surface
because of important anatomical information omitted
in the process to get 3D space. Thus, the need arises to
the development of 3D segmentationalgorithms for
more accurate segmentation ofvolumetric imagery.
The importantdifference between 2D and3D
image segmentation is in the processing elements,
pixels/voxels, respectively, and their 2D or 3D
neighborhoodsoverwhich
image
features
are
calculated.[24][23]
This paper includes, standard methods commonly
used for brain MRI segmentation in section II. Main
literature survey of some papers in section III
followed by section IV, critical evaluation, table on
comparison of segmentation methods of brain tumor
in which, we have highlighted differences between
them and discuss their benefits and limitations in the
table 1and then in section V we deduce a conclusion
based on our studies on the literature on the topic
mentioned above.
II CONVENTIONAL METHODS
A wide variety of segmentation methodshas been
proposed but still there is no standard approach which
results efficiently. Few of these methods are briefly
listed below:
Thresolding: It is simplest and the oldest method of
image segmentation. It is based on the assumption
that adjacent pixels whose value lies within a certain
range belong to the same class which gives good
segmentation of images that include only two opposite
components can be obtained. [25]Threshold based
image segmentation are Global, Local and Adaptive
Thresholding. The key parameter is the choice of
selecting threshold value T. This approach helps in the
proper detection of the region of interest. The main
limitation, only two classes are generated and it
cannot be used for multi-channel images. It is not
capable of exploiting the information provided by
MRI. It approach is sensitive to noise and intensity
homogeneities.
Region Growing: For image segmentation region
growing method is a well-developed technique. Based
on some predefined criteria this method extracts
region of interest. This is based on intensity
information or edges in the image. [25]It starts with a
seed, which is selected in the centre region of interest.
During the region growing state, pixels in the
neighbor of seed are added to region based on
homogeneity criteria thereby resulting in a connected
region. This methodis sensitive to noise, causing
extracted regions to have holes or even become
disconnected. These problems can be removed using a
homotopic region-growing algorithm.
Watershed segmentation:It can be explained by a
metaphor based on the behavior of water in a
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landscape. [19] When it rains, drops of water falling in
different regions will follow the landscape downhill.
For each valley there will be a region from which all
water drains into it. At points where water comes from
different basins meets, dams will be built. The process
is stop, when the water level reaches the highest peak
in the landscape. As a result, the landscape is
partitioned into regions separated by dams, called
watershed lines. Some researchers used multi-scale
watershed transformation to segment brain tumors
Kmeans clustering: The K-means clustering
algorithm clusters data by iteratively computing a
mean intensity for each class and segmenting the
image by classifying each pixel in the class with the
closest mean. [25]This is also called as hard
segmentation. A hard segmentation forces a decision
of whether a pixel is inside or outside the object.
Fuzzy C –Means(FCM): Segmentations that allow
regions or classes to overlap are called soft
segmentations. Soft segmentations are important in
medical imaging because of partial volume effects,
where multiple tissues contribute to a single pixel or
voxel resulting in a blurring of intensity across
boundaries.[25][19] The main objective is to develop
an FCM algorithm for distributing the clusters such
that it obtains group of clusters minimizes the
dissimilar elements in each cluster .It classifies pixels
of an image data set into clusters based on Euclidean
distance of a pixel from the center of the least distant
cluster.
Markov Random Field Algorithm (MRF): It is a
type of unsupervised clustering method.[26]It
provides integration of clustering process with spatial
information of pixels. It reduces the problem of
clusters overlapping and noise effect.
Classifier methods: supervised methods are
pattern recognition techniques that partition a feature
space derived from the image by using data with
known labels.[25] A simple classifier is the nearestneighbor classifier, in which each pixel is classified in
the same class as the training datum with the closest
intensity. The k-nearest-neighbor classifier is a
generalization of this approach.
Support Vector Regression (SVR): It has the
ability of learning the nonlinear distribution of the
image data without prior knowledge,[19] via the
automatic procedure of SVM parameters training and
an implicit learning kernel and achieved better
segmentation results for the extraction of the brain
tumors, compared to the fuzzy clustering method.
Guassian Mixture Model: Here the voxels
intensity in each target region are modelled by a
Guassian distribution and the GMM parameters are
usually estimated by maximizing the likelihood of the
observed image via the EM algorithm. On the other
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hand, statistical methods often relied on Gaussian
assumptions in most cases, using which for modelling
the underlying distributions. Bayesian inference has
been commonly applied to classify the tissue with
MRI.[25][26] Many modified version of the mixture
model algorithms had been proposed. There are also
many literatures using statistical mixture modelling
with expectation-maximization algorithm includes.
II LITERATURE REVIEW
In this section, review of the selected
literature on image segmentation techniques and their
usage is mentioned. The main purpose is to highlight
key strengths and limitations to these techniques.
correct classification. It requires far fewer iterations to
converge as compared to EM & FCM.As it is
capability
to
deal
with
noise,
it produceslittle better but noticeable results than
EM.BCFCM outperformed the FCM on both
simulated and real MRI images. However, FCM has
the advantage of working for vectors of intensities
whereas the BCFCM is limited to single-feature
inputs.Results presented are preliminary and need
proper clinical evaluation. However, this method
involves phantom measurement based on global
corrections for image non-uniformity. The author
claims, further work is needed for localized
measurement like impact on tumor boundary or
volume determinations.
Yongyue Zhang et al. [1] paper shows, use of
Hidden Markov Random Field (HMRF) model for
segmenting Brain MRI by using ExpectationMaximization algorithm.As it has the ability to encode
both the statistical and spatial properties of an image,
HMRF model is flexible for image modeling.In this
paper, author claims, the HMRF-EM method
overcomes almost all the drawbacks associated with
FM-EM method. The experimental results prove that
HMRF-EM produced promising results even with
high level of noise, low image quality and large
number of classes. A key limitation of this research is
that preliminary estimations based on threshold are
purely heuristic. However, in case of high invariability
of brain MRI, it fails to generate perfect results,
particularly in terms of contrast between brain tissues
and intensity ranges and this will lead inaccuracy in
EM segmentation. Apart from this the proposed
method is time consuming and inaccurate most of the
times. However, it can obtain slightly good speed by
utilizing ICM deterministic method.
Mohammed F. Tolba et al. [3] in their paper
proposed a new segmentation algorithm for MRI brain
image, which is based on EM algorithm and the multiresolution analysis
of
images,
known
as
Gaussian multi-resolution EM
algorithm.
The
evidence from experiments result show that
performance of the proposed technique is much better
than the conventional EM algorithm.To overcome the
issues mentioned, an algorithm is proposed named
GMEM. Merit of the proposed method that it is less
sensitive to the noise level and can be used for noisy
images. Additionally, accurate results can be obtained,
using GMEM algorithm many other medical imaging
techniques.The authors argue that more reliable
segmentation algorithm can be formulated using
advanced scientific techniques, such as data
fusion.However, a limitation to this technique is that
when the GMEM algorithm is applied to pixel laying
on the boundaries between classes or on edges,
generates many misclassified pixels.
Mohamed N. Ahmed et al. [2] present
customized algorithm for fuzzy segmentation of MRI
data along with the estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can lead to imperfections in the radiofrequency coils or to problems associated with the
acquisition sequences. The result is a slowly varying
shading artifact (noise) over the image that can
produce errors with conventional intensity-based
classification.The proposed algorithm is modeled by
modifying in the objective function of the standard
fuzzy c-means algorithm to compensateinhomogeneities and to allow the labeling of a pixel to
be influenced by the labels in its immediate
neighborhood. This neighborhood effect acts as
a regularizer and biases the solution toward piecewise
in-homogeneous labelings. Experimental results prove
the evidence of effectiveness and efficiency on both
synthetic images and MR data. From this we deduce
that the regularization can overcome the major
drawback of FCM in-homogeneities. The major
contribution of proposed work is the introduction of a
BCFCM algorithm which is faster to converge to the
Li et al. [4] in their paper showed that many
vision-related processing tasks such as edge detection,
image segmentation and stereo matching are not
possible to achieve in optical lenses that have long
focal lengths which have a limited depth of field.
Previously, work has been done for this mechanism,
one of which is wavelet-based image fusion.
According to the proposed method, collect several
source images with different focuses of the same
scene and then process it with the discrete wavelet
transform (DWT) and the support vector machines
(SVM). Features extracted from the DWFT
coefficients are further used,SVM is trained to select
the source image having the best focus at each pixel
location, and this corresponding DWFT coefficients
are then included into the composite wavelet
representation. Among these wavelet decompositions,
the wavelet coefficient having the largest magnitude is
selected at each pixel location.Finally, author claims
that, the fused image can be recovered by performing
the inverse DWT whereas the wavelet function can be
improved by applying discrete wavelet frame
transform (DWFT) and support vector machine
(SVM).Author claims, the experimental results show,
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the proposed method outperforms the traditional
approach both visually and quantitatively.
Sing et al. [5] reported that the quality of MRI
gets affected by intensity in-homogeneities generated
duringacquisition process which makes segmentation
task more difficult. The paper proposed a method for
segmentation of MRI using a fuzzy adaptive radial
basis function neural network (FARBF-NN). The
scholar used an interesting method, to eliminate
theeffect of noise present in the input image using a
fuzzy membership function which in turn modify the
outputs of hidden layer neurons of the FARBF-NN.
Study shows that the proposed method is better than
the k-means clustering algorithm, the fuzzy c-means
(FCM) clustering algorithm. The paper gave an idea
that Hidden layer neuron of FARBF-NN neurons can
be fuzzified to reduce noise effect.
MG Di Bono et al.[6] describes a method
based on Support Vector machines for Regression
(SVR) to decode cognitive states from functional
Magnetic
Resonance
Imaging
(fMRI)
data.Their focus
is
that
a
comprehensive
methodology is required to explore the feasibility of
the SVR Kernel-based approach for extremely
complex regression problem.The authors created
virtual environment to get subjective feature and
objective measures. FMRI data was collected for
prediction of ratings. For each subject, the need
arises to predict feature separately.After applying
SVM Regression, with the help of applying statistical
measures
to
achieve
enhanced
performanceand generalizability. However, more
accuracy can be achieved by statistical techniques
such
as
sorting,
distributions
(chi-square,
binomial).Moreover, virtual environment has its own
shortcomings and special considerations are the
major reason of inaccuracy.
Yu et al. [7] proposed the methodology of
segmenting an image into three parts, including dark,
gray and white. For 2D histogram, Z-function and sfunction are used for fuzzy division. Afterwards,
(Quantum Genetic Algorithm) QGA is applicable for
finding combination of 12 membership parameters,
which have maximum value. This technique enhances
the image segmentation. The significance of their
work is the use of three-level segmentation followed
by maximum fuzzy partition of 2D Histograms.
Proposed method is the selection of for optimal
combination of parameters with the fuzzy partition
entropy of 2D histogram.This generates better
performance
than
one
dimensional
3-level
thresholding method. Somehow, it is not practically
feasible as a large number of possible combinations of
12 parameters in a multi-dimensional fuzzy partition
are used. Therefore, QGA can be used to find the
optimal combination.
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M.Kumar et al.[8] reported that all automatic
seed finding methods may suffer, if there is no
growth of tumor and any small white portion present
and when the edges of tumor is not sharped then the
segmentation results over segmented or under
segmented. This may occur due to initial stage of the
tumors. Outranging this problem, author proposed
method of tumor detection based on texture of the
MRI. Their method is intended to help the surgeon to
distinguish the involved area precisely by separating
the irregularity from the regular surrounding tissue to
get a real identification of involved and noninvolved
area. The method used is texture analysis and seeded
region growing method. It gives new, robust, fast and
fully automatic algorithm where algorithm needs no
prior information or training process. By taking into
account both the homogeneous texture features and
spatial features of the MRI, their work find the seed
points and the segmentation results obtained are very
much accurate. There are very few pixels which are
misclassified. However, a limitation to this technique
is that time consumption could be reduced.
Roy et al.[9] proposed automatic brain tumor
detection approach using symmetry analysis. The
sequence of the methodology proposed in paper shows
detection of tumor at the beginning then segmenting
the area of interest and followed by area of tumor
calculation.One of the important aspects is that after
performing the quantitative analysis, we can identify
the status of an increase in the disease. This Paper
suggested multi-step and modular approached to solve
the complex MRI segmentation problem.They proved
that their algorithm can automatically detect and
segment the brain tumor. Like most authors, the
authors agree that MR imaging gives better result
compare to other techniques like CT images and Xrays. To remove noise, image pre-processing isdone
by converting RGB image into grayscale image and
then passing that image to the high pass filter.
Anam Mustaqeem et al. [10] emphasized
efficient algorithm for tumor detection based on
thresholdsegmentation and morphological operators.
At First quality of scanned image is enhanced and
then morphological operators are applied to detect the
tumor in the scanned image. Additionally, the main
logic is the selection of a threshold value. Previous
Researched methods can be used under this
segmentation, maximum entropy method and kmeans clustering method for segmentation. It gives
interesting result, ability to detect the continuous
boundary of the region of interest. It is easy to execute
and manage thus applicable for high accuracy and
precision is needed. The study concludes, selection of
threshold value is difficult. However, Location of
tumor
can
be
specified
easily
using thresholding bydetermining an intensity value
for specifying groups.
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J. Selvakumar et al. [11] paper described the
implementation of Simple Cluster Algorithm for
detection of range and shape of tumor in brain
MRI.Proposed method combines the two computer
aided methods for segmentation. At the end of the
method, tumor is extracted from the MR image and its
exact position and the shape is determined & the
tumor’s stage is estimated based on the amount of area
calculated from the cluster.The method proposed
shows, if tumor has a mass then K- means algorithm is
enough to extract it from the brain cells. And if there
is any presence of noise in the MR image, it is
removed before the K-means process. The noise free
image is an input to the k-means and tumor is
extracted from the MRI image followed by
segmentation using Fuzzy C means for accurate tumor
shape extraction. Thresholding is used as a last stage
for feature extraction at the output.Finally, the author
claims the approximate reasoning for calculating
tumor shape and position calculation is required. The
experimental results were compared with other
algorithms and the proposed method is more accurate
was proved. However, in future 3D assessment of
brain using 3D slicers can be developed with matlab.
Sonu Suhag et al.[14] paper proposed, like
most of the researchers, work carried out for
processing of MRI brain images for detection and
classification of tumor and non-tumor image is done;
however, here it is by using classifier. The image
processing is carried out first by using any of the
standard methods and then for segmentation Fuzzy C
mean segmentation method, feature extraction using
GLCM technique and SVM Classifier is used. Study
shows that it will determine whether it is normal or
abnormal. This combination gave accuracy of around
94% in classifying whether the MRI image is normal
or abnormal.
Hooda et al. [13] paper deals with
theperformance analysis of image segmentation
techniques such as K-Means Clustering, Fuzzy CMeans Clustering and Region Growing for detection
of brain tumor.In this paper, performance evaluation
of the above mentioned techniques is done based on
error percentage as compared to ground truth.
After comparing all the three methods it was
concluded that the error percentage value was lowest
with FCM clustering and it outperforms other
segmentation algorithm.
Ajala Funmilola A [15] focused attention
onClustering methods, specifically k-means and fuzzy
c-means clustering algorithms. The combination of
these algorithms provides a novel proposed method
called fuzzy k-c-means clustering algorithm, which
has a better result in terms of time utilization.Time,
accuracy, and iterations have been the major
focus. Still, limitations like k-means segmenting
withpredetermined number of clusters and Fuzzy Cmeansgenerating an overlapping results persist and not
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being able to segment colored images until they are
convertedinto grey scale. Fuzzy K-C-Means also
operates almost like FuzzyC-Means.
Roopali R. Laddha et al. [17] paper proposed
a method to detect brain tumor using medical imaging
techniques. Techniques focused in this paper were
Text- Noise Removal & segmentation which can
include a method based onthreshold segmentation,
watershed segmentation and morphological operators.
The proposed segmentation method was experimented
with MRI scanned images of human brains, thus
locating tumor in the images. Samples of human
brains were taken, scanned using MRI process and
then wereprocessed through segmentation methods.
Harneet Kaur and Sukhwinder Kaur [18]
argued that the most of existing methods has ignored
the poor quality images like images with noise or poor
brightness.So to overcome these limitations, a new
technique has been proposed. The comparison has
shown that the proposed technique has achieved up to
94 % accuracy which was only 78 % in neural based
technique. Also, for high corrupted noisy images the
proposed technique has shown quite effective results
than the neural based tumor detection. The study
shows, work proposed uses new object based brain
tumor detection and the decision based median
filtering technique. Authors claim that their proposed
method shows quite effective results than neural based
tumor detection technique.
Gauri et al.[16] paper reports a survey on
different segmentation techniques applied to MR
Images for locating tumor and a proposed method for
the same using Fuzzy C-Means algorithm along with
an algorithm to find area of tumor which is useful to
decide type of brain tumor. By finding area of tumor
we can decide type of tumor;whether it is benign or
malignant.
Khan [23] has widely researched and
provide us with the efficient and impressive literature
survey and validated the outcome that yet the field of
research has vast to do get a perfect method for image
segmentation because the result of image
segmentation is dependent on many factors which
varies with the method implemented. i.e., pixel color,
texture, intensity, similarity of images, image content,
and problem domain. Author also claim, it is good to
use hybrid solution consists of multiple methods for
image segmentation problem.
Jin Liu et al. [19] provided a very interesting
comprehensive overview for MRI-based brain tumor
segmentation methods. The paper includes an
introduction to brain tumors and imaging modalities
of brain tumors, preprocessing operations and briefly
explained the methods of MRI-based brain tumor
segmentation.
Moreover, the evaluation and
validation of the results of MRI-based brain tumor
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segmentation are discussed. Finally, an objective
assessment is presented and future developments and
trends are addressed for MRI-based brain tumor
segmentation methods. Although most of brain tumor
segmentation algorithms have relatively good results
in the field of medical image analysis, there is a
certain distance in clinical applications. Due to a lack
of interaction between researchers and clinicians,
clinicians still rely on manual segmentation for brain
tumor in many cases.
Gaussian mixture models, wavelet based models,
finite mixture models, fuzzy adaptive, many hybrid
models, etc.
Most of the researchers’ preferred MR
images due to its advantages over other techniques
such as MRI provides rich information about
anatomical
structure,
enabling
quantitative
pathological or clinical studies. A critical review of
the studied literature is summarized in the overview of
segmentationtable. (Table 1).
III CRITICAL EVALUATION
In this study, we have seen different
techniques for segmentation. The prominent intensity
models studied in this paper include neural networks,
TABLE- 1 COMPARISION OF SEGMENTATION METHODS
Summary
Proposed
Technique
Segmentation Zhang [1] (2001)
of MRI images Hidden Markov
Random FieldExpectation
Maximization
(HMRFEM)model
Segmentation Ahmed[2](2002)
of MRI images
Bias-Field
Estimation
Segmentation Tolba[3] (2003)
of MRI images GaussianMulti
resolutionAnalysi
s
Fusing
Li[4](2004)
images
Discrete Wavelet
Transform (DWT)
& Support Vector
Machines (SVM)
Segmentation J. K. Sing[5]
of MRI images (2005)
Neutral Network
Algorithm
Used
Benefits Identified
Problems
Expectation
Maximization
It can encode both spatial and
statistical properties of an
image, HMRF is flexible
model, Efficient result even
with high level of noise, low
image quality and large
number of classes
It produces better results for
noisy images than the EM
algorithm, Fast convergence,
Overcomes intensity inhomogeneities in FCM
Requires estimating
threshold which is
heuristic, Results not
accurate most of the time,
Time consuming,
Expensive
QGA is selected for optimal
combination of parameters
with the fuzzy partition
entropy, Performance wise
better than one dimensional
3-level thresholding method
Practically not feasible
(Bias-Corrected
Technique is limited to a
Fuzzy C- Mean)
single feature input,
BCFCM
Incorporation of spatial
Algorithm
constraints into
/Modified
theclassification blurs
Fuzzy C-Mean
some fine details
Expectation
Performance wise GMEM is Rarely preserve edges,
Maximization
better than EM, Less sensitive Produces misclassification
to noise, Used in many
at boundaries between
medical techniques
classes
Discrete
Technique uses enhanced Wavelet frame version of DWT, Relatively
transform
easy to implement, More
accurate, It is not affected by
consistency verification and
activity level measurements
Fuzzy Adaptive It removes effect of noise,
Only one task related to
radial
basis Preserving sharpness of
fusion can be done
function
image, Better result than K(FARBF-NN )
mean and FCM method
Support Vector It uses statistical technique
Inaccurate due to virtual
Regression
environment.
Decoding
Cognitive
States
MG Di Bono[6]
(2008)
Meanintensity
Three-level
Image
Segmentation
Yu[7] (2008)
Quantum
Fuzzy
partition Genetic
entropy of 2D Algorithm
histogram
and ( QGA )
geneticalgorithm
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Texture based
segmentation
from MRI
images
Kumar[8] (2011)
Seeded Region
growing method
Segmentation Roy[9] (2012)
of MRI images Modular approach
Segmentation
of MRI
images
Segmentation
of MRI
images
Segmentation
of MRI
images
Selva [11] (2012)
Advanced KMean Clustering
and Fuzzy CMean Algorithm
Anam [10] (2012)
Combination of
threshold and
watershed
segmentation
Funmilola [12]
(2012) Fuzzy k-cmeans Clustering
Algorithm
Segmentation
of MRI
images
Kaur (2014)[18]
Object Based
Brain tumor
segmentation
Segmentation Gauri[16] (2013)
of MRI
Fuzzy C-Means
images
Segmentation
Segmentation Barbudhe (2014)
of MRI
[12] Gaussian
images
Multi resolution
EM algorithm
Segmentation Hooda (2014)[13]
of MRI
K-Means, Fuzzy
images
C-Means
Clustering and
Region Growing
Segmentation Suhag (2015)[14]
of MRI
Fuzzy C mean
images
segmentation
method
IV CONCLUSION
Seeded Region
growing
algorithm
Determines the abnormality
presence, Fully automatic
algorithm, no prior
information or training
process needed.
Automatically detect different
types of brain tumors with
anefficient results, uses
quantitative analysis.
Time consuming, Few
pixels which are
misclassified
Faster realization, Exact
shape and position is
determined
complex Methodology
Useful
for
image
binarization, to locate of
tumor,
able
to
detect
continuous boundary of the
region of interest, Easy to
execute and manage, obtain
high accuracy and precision.
Selection of threshold
value is difficult
K-Mean
clustering is
followed by
FCM
Time, accuracy, and iterations
have been the major focus
object based
brain tumor
segmentation
Reduces the problem of noise
and gives accurate result
Predetermined number of
clusters, FCM generates
overlapping results, Not
able to segment colored
images.
can be further improved
with Fuzzy technique
FCM algorithm
Good Accuracy, Automatic
method
Expectation
Maximization
Algorithm
Utilizes the spatial correlation
between neighboring pixels,
reduces computational
complexity, high reliability.
The error percentage value
was lowest with FCM
clustering and it outperforms
other segmentation algorithm.
Symmetry
analysis(waters
hed and
threshold
segmentation)
Simple Cluster
Algorithm (KMean + FCM)
Thresholding
(feature extract)
maximum
entropy, kmeansclustering
method and
morphological
tools
K-Means,
Fuzzy C-Means
and Region
Growing
Algorithm
Combination of
FCM algorithm,
GLCM & SVM
technique
Radiologists useMRI as medical imaging
technique and for visualization of the internal
structure of the body. Good sort of information related
to human brain soft tissue anatomy, useful for
diagnosis of brain tumor can be obtained easily.
Further, MR images are used to analyze and study
behavior of the brain.In diagnosis, image
segmentation is inseparable part for tumor detection
and till date many researchers have work upon it from
more than decades. The contribution of all the
ISSN: 2231-5381
Determines the abnormality
presence, The combination
give accuracy of around 94%
Time consuming, Tedious
Poor contrast, Noise and
intensity in-homogeneities
can affect the results
It
generates
many
misclassified pixels
-
-
intelligent and novel work is tremendous in this field
but yet a method is not sufficient to detect and
segment the complex anatomy of brain with
approximately 120 different types of tumors.
From studies, we deduce that a hybrid
method seems to be the only solution for getting a
universal method for tumor segmentation. Many work
on 3D space is performed, with the view to overcome
the issues leading to the increase in complexity of the
method and slower computational time.Due to the
complexity and understanding the sensitivity and
robustness of the tumor detection for a life of patient,
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
applications, ISSN 2224-5782,ISSN 2224-0506. Vol 2, no.6,
2012
[16] Gauri P. Anandgaonka and Ganesh.S.Sable― Detection
andidentification of brain tumor in brain MR images using
ACKNOWLEDGMENT
Fuzzyc-means segmentation, international journal of advanced
research in computer and communication engineering vol. 2,
I would like to thank my guide,
issue 10, october 2013
Dr.S.R.Suralkarand supporting staff of SSBT E&TC [17] Miss. Roopali R. Laddha and Dr. Siddharth A. Ladhake ―Brain
tumor detection using morphological and watershed operators,
department, Jalgaon for their valuable guidance and
ijaiem, volume 3, issue 3, march 2014
precious time allotted me for this work.
[18] Harneet Kaur and Sukhwinder Kaur, ―Improved Brain Tumor
Detection Using Object Based Segmentation‖, IJETT,
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