International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
Efficient Brain Tumor Segmentation using Support
Vector Machines
Geeta
M.Tech Scholar & GIET,Sonipat (DCRUST Murthal,)
Sonipat Haryana,India
Abstract— Segmentation of anatomical elements of brain is the
fundamental problem in health image analysis. The aim of this
work is to create an automated method for mind tumor
quantification using MRI picture data units using support vector
machines. A brain tumor segmentation method has become
developed and validate segmentation on 2D & 3D MRI Data.
This technique doesn't require any initialization while the others
require an initialization inside the tumor. In this, after a manual
segmentation procedure the tumor identification, the
investigations was made for the prospective usage of MRI data
for improving mind tumor shape approximation and 2D & 3D
visualization for surgical planning and assessing tumor. Medical
planning now uses both 2D & 3D designs that integrate data from
several imaging modalities.
Index Terms—Brain Segmentation, MRI imaging, Support
Vector Machines, Classification.
I.
II.
INTRODUCTION
Image segmentation is an vital processing pace in
countless picture, video and computer vision applications.
Comprehensive analysis has been completed in crafting
countless disparate ways and algorithms for picture
segmentation, but it is yet tough to assess whether one
algorithm produces extra precise segmentations than one
more, whether it be for a particular picture or set of pictures,
or extra usually, for a finished class of images. To date, the
most public method for assessing the effectiveness of a
segmentation method is subjective evaluation, in that a human
visually assesses the picture segmentation aftermath for
distinct segmentation algorithms, that is a monotonous
procedure and inherently limits the depth of evaluation to a
moderately tiny number of segmentation comparisons above a
predetermined set of images. One more public evaluation
alternative is supervised evaluation, in that a segmented
picture is contrasted opposing a manually segmented or preprocessed reference picture [1].
It is frequently utilized to partition an picture into distinct
spans, that ideally correspond to disparate real-world objects.
It is a critical pace towards content analysis and picture
understanding. Countless segmentation methods have been
industrialized, but there is yet no satisfactory presentation
compute, that makes it hard to difference disparate
segmentation methods, or even disparate parameterizations of
a solitary method. Though, the skill to difference two
segmentations in an application-independent method is
important:
to independently select amid two probable
segmentations inside a segmentation algorithm or a broader
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application; to locale a new or continuing segmentation
algorithm on a solid experimental and logical ground; and to
monitor segmentation aftermath on the hover, so that
segmentation presentation can be guaranteed and consistency
can be maintained.
Designing a good compute for segmentation quality is a
recognized hard problem—some researchers even sense it is
impossible. Every single person has different average for a
good segmentation and disparate requests could purpose larger
employing disparate segmentations. As the criteria of a good
segmentation are frequently application-dependent and hard to
explicitly delineate, for countless requests the difference amid
a favorable segmentation and an inferior one is noticeable. It
is probable (and necessary) to design presentation measures to
arrest such differences.
IMAGE SEGMENTATION PROBLEM
The heavy use of camera phones and supplementary
mobile mechanisms all above the globe has produced a
marketplace for mobile picture analysis, encompassing picture
segmentation to distinct out objects of interest. Automatic
picture segmentation algorithms, after retained by countless
disparate users for several requests, cannot promise elevated
quality results. Yet interactive algorithms need human power
that could come to be quite tedious. To cut human power and
accomplish larger aftermath, it is worthwhile to understand in
advance that pictures are tough to segment and could need
more user contact or alternate processing. For this patriotic,
we familiarize a new analysis problem: how to guesstimate the
picture segmentation difficulty level lacking truly giving
picture segmentation [2].
Image segmentation is an vital and unsolved analysis area.
In computer vision, automatic segmentation algorithms target
to tear an picture into meaningful spans for requests such as
pursuing or recognition They frequently involve supervised
training to adjust the parameters for a particular application.
Even alongside all these efforts, they are not guaranteed to
work well on all pictures and, in finish, do not present as well
as humans.
.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
Figure 1 Difficulty levels can estimate the machine
segmentation? (a) Easy (b) Fair, (c) Hard
I.
RELATED WORK
Gholipour, A. et al, in "Brain Functional Localization: A
Survey of Image Registration Techniques" 2007 [1], the
authors describe Functional localization is a concept which
involves the application of a sequence of geometrical and
statistical image processing operations in order to define the
location of brain activity or to produce functional/parametric
maps with respect to the brain structure or anatomy.
Considering that functional brain images do not normally
convey detailed structural information and, thus, do not
present an anatomically specific localization of functional
activity, various image registration techniques are introduced
in the literature for the purpose of mapping functional activity
into an anatomical image or a brain atlas. The problems
addressed by these techniques differ depending on the
application and the type of analysis, i.e., single-subject versus
group analysis. Functional to anatomical brain image
registration is the core part of functional localization in most
applications and is accompanied by intersubject and subjectto-atlas registration for group analysis studies. Cortical surface
registration and automatic brain labeling are some of the other
tools towards establishing a fully automatic functional
localization procedure. While several previous survey papers
have reviewed and classified general-purpose medical image
registration techniques, this paper provides an overview of
brain functional localization along with a survey and
classification of the image registration techniques related to
this problem
Ribbens, A. et al, in "Unsupervised Segmentation,
Clustering, and Groupwise Registration of Heterogeneous
Populations of Brain MR Images" 2014 [2], the authors
describe Population analysis of brain morphology from
magnetic resonance images contributes to the study and
understanding of neurological diseases. Such analysis
typically involves segmentation of a large set of images and
comparisons of these segmentations between relevant
subgroups of images (e.g., ). The images of each subgroup are
usually selected in advance in a supervised way based on
clinical knowledge. Their segmentations are typically guided
by one or more available atlases, assumed to be suitable for
the images at hand. They present a data-driven probabilistic
framework that simultaneously performs atlas-guided
segmentation of a heterogeneous set of brain MR images and
clusters the images in homogeneous subgroups, while
constructing separate probabilistic atlases for each cluster to
guide the segmentation. The main benefits of integrating
segmentation, clustering and atlas construction in a single
framework are that: 1) their method can handle images of a
heterogeneous group of subjects and automatically identifies
homogeneous subgroups in an unsupervised way with
minimal prior knowledge, 2) the subgroups are formed by
automatical detection of the relevant morphological features
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based on the segmentation, 3) the atlases used by their method
are constructed from the images themselves and optimally
adapted for guiding the segmentation of each subgroup, and 4)
the probabilistic atlases represent the morphological pattern
that is specific for each subgroup and expose the groupwise
differences between different subgroups. They demonstrate
the feasibility of the proposed framework and evaluate its
performance with respect to image segmentation, clustering
and atlas construction on simulated and real data sets
including the publicly available BrainWeb and ADNI data. It
is shown that combined segmentation and atlas construction
leads to improved segmentation accuracy. Furthermore, it is
demonstrated that the clusters gene- ated by their unsupervised
framework largely coincide with the clinically determined
subgroups in case of disease-specific differences in brain
morphology and that the differences between the clusterspecific atlases are in agreement with the expected diseasespecific patterns, indicating that their method is capable of
detecting the different modes in a population. Our method can
thus be seen as a comprehensive image-driven population
analysis framework that can contribute to the detection of
novel subgroups and distinctive image features, potentially
leading to new insights in the brain development and disease.
Shu Liao et al, in "Sparse Patch-Based Label Propagation
for Accurate Prostate Localization in CT Images" 2013
[3], the authors describe In this paper, they propose a new
prostate computed tomography (CT) segmentation method for
image guided radiation therapy. The main contributions of
their method lie in the following aspects. 1) Instead of using
voxel intensity information alone, patch-based representation
in the discriminative feature space with logistic sparse LASSO
is used as anatomical signature to deal with low contrast
problem in prostate CT images. 2) Based on the proposed
patch-based signature, a new multi-atlases label fusion method
formulated under sparse representation framework is designed
to segment prostate in the new treatment images, with
guidance from the previous segmented images of the same
patient. This method estimates the prostate likelihood of each
voxel in the new treatment image from its nearby candidate
voxels in the previous segmented images, based on the
nonlocal mean principle and sparsity constraint. 3) A
hierarchical labeling strategy is further designed to perform
label fusion, where voxels with high confidence are first
labeled for providing useful context information in the same
image for aiding the labeling of the remaining voxels. 4) An
online update mechanism is finally adopted to progressively
collect more patient-specific information from newly
segmented treatment images of the same patient, for adaptive
and more accurate segmentation. The proposed method has
been extensively evaluated on a prostate CT image database
consisting of 24 patients where each patient has more than 10
treatment images, and further compared with several state-ofthe-art prostate CT segmentation algorithms using various
evaluation metrics. Experimental results demonstrate that the
proposed method consistently achieves higher segmentation
accuracy than any other methods under comparison.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
Nitzken, M.J. et al, in "Shape Analysis of the Human
Brain: A Brief Survey" 2014 [4], the authors describe The
survey outlines and compares popular computational
techniques for quantitative description of shapes of major
structural parts of the human brain, including medial axis and
skeletal analysis, geodesic distances, Procrustes analysis,
deformable models, spherical harmonics, and deformation
morphometry, as well as other less widely used techniques.
Their advantages, drawbacks, and emerging trends, as well as
results of applications, in particular, for computer-aided
diagnostics, are discussed.
Wenjia Bai et al, in "A Probabilistic Patch-Based Label
Fusion Model for Multi-Atlas Segmentation With
Registration Refinement: Application to Cardiac MR
Images" 2013 [5], the authors describe The evaluation of
ventricular function is important for the diagnosis of
cardiovascular diseases. It typically involves measurement of
the left ventricular (LV) mass and LV cavity volume. Manual
delineation of the myocardial contours is time-consuming and
dependent on the subjective experience of the expert observer.
In this paper, a multi-atlas method is proposed for cardiac
magnetic resonance (MR) image segmentation. The proposed
method is novel in two aspects. First, it formulates a patchbased label fusion model in a Bayesian framework. Second, it
improves image registration accuracy by utilizing label
information, which leads to improvement of segmentation
accuracy. The proposed method was evaluated on a cardiac
MR image set of 28 subjects. The average Dice overlap metric
of their segmentation is 0.92 for the LV cavity, 0.89 for the
right ventricular cavity and 0.82 for the myocardium. The
results show that the proposed method is able to provide
accurate information for clinical diagnosis.
Sabuncu, M.R. et al, in "A Generative Model for Image
Segmentation Based on Label Fusion" 2010 [6], the authors
describe They propose a nonparametric, probabilistic model
for the automatic segmentation of medical images, given a
training set of images and corresponding label maps. The
resulting inference algorithms rely on pairwise registrations
between the test image and individual training images. The
training labels are then transferred to the test image and fused
to compute the final segmentation of the test subject. Such
label fusion methods have been shown to yield accurate
segmentation, since the use of multiple registrations captures
greater inter-subject anatomical variability and improves
robustness against occasional registration failures. To the best
of their knowledge, this manuscript presents the first
comprehensive probabilistic framework that rigorously
motivates label fusion as a segmentation approach. The
proposed framework allows them to compare different label
fusion algorithms theoretically and practically. In particular,
recent label fusion or multiatlas segmentation algorithms are
interpreted as special cases of their framework. They conduct
two sets of experiments to validate the proposed methods. In
the first set of experiments, they use 39 brain MRI scans with manually segmented white matter, cerebral cortex,
ventricles and subcortical structures - to compare different
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label fusion algorithms and the widely-used FreeSurfer wholebrain segmentation tool. Our results indicate that the proposed
framework yields more accurate segmentation than FreeSurfer
and previous label fusion algorithms. In a second experiment,
they use brain MRI scans of 282 subjects to demonstrate that
the proposed segmentation tool is sufficiently sensitive to
robustly detect hippocampal volume changes in a study of
aging and Alzheimer's Disease.
Albert
Huang,
A.
et
al,
in
"A
Hybrid
Geometric–Statistical Deformable Model for
Automated 3-D Segmentation in Brain MRI" 2009 [7], the
authors describe They present a novel 3-D deformable modelbased approach for accurate, robust, and automated tissue
segmentation of brain MRI data of single as well as multiple
magnetic resonance sequences. The main contribution of this
study is that they employ an edge-based geodesic active
contour for the segmentation task by integrating both image
edge geometry and voxel statistical homogeneity into a novel
hybrid geometric-statistical feature to regularize contour
convergence and extract complex anatomical structures. They
validate the accuracy of the segmentation results on simulated
brain MRI scans of both single T1-weighted and multiple
T1/T2/PD-weighted sequences. They also demonstrate the
robustness of the proposed method when applied to clinical
brain MRI scans. When compared to a current state-of-the-art
region-based level-set segmentation formulation, their white
matter and gray matter segmentation resulted in significantly
higher accuracy levels with a mean improvement in Dice
similarity indexes of 8.55% (p<0.0001) and 10.18%
(p<0.0001), respectively.
Long Chen et al, in "A Multiple-Kernel Fuzzy C-Means
Algorithm for Image Segmentation" 2011 [8], the authors
describe In this paper, a generalized multiple-kernel fuzzy Cmeans (FCM) (MKFCM) methodology is introduced as a
framework for image-segmentation problems. In the
framework, aside from the fact that the composite kernels are
used in the kernel FCM (KFCM), a linear combination of
multiple kernels is proposed and the updating rules for the
linear coefficients of the composite kernel are derived as well.
The proposed MKFCM algorithm provides them a new
flexible vehicle to fuse different pixel information in imagesegmentation problems. That is, different pixel information
represented by different kernels is combined in the kernel
space to produce a new kernel. It is shown that two successful
enhanced KFCM-based image-segmentation algorithms are
special cases of MKFCM. Several new segmentation
algorithms are also derived from the proposed MKFCM
framework. Simulations on the segmentation of synthetic and
medical images demonstrate the flexibility and advantages of
MKFCM-based approaches.
Li Yi et al, in "A review of segmentation method for MR
image" 2010 [9], the authors describe Magnetic resonance
(MR) is a typical medical imaging technique. It can provide
high resolution 3D image with anatomical and function
information through analyzing MRI sequence, which
facilitates and improves diagnosis and patient treatment. The
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
first important step in image analysis is image segmentation.
In this paper, numerous methods that have been developed for
segmentation in MRI are reviewed. They study these
segmentation strategies and perform a qualitative discussion
according to 3 categories, i.e. traditional image processing
method, statistical-based segmentation method and partition
technique with bias field estimation.
Zexuan Ji et al, in "Fuzzy Local Gaussian Mixture Model
for Brain MR Image Segmentation" 2012 [10], the authors
describe Accurate brain tissue segmentation from magnetic
resonance (MR) images is an essential step in quantitative
brain image analysis. However, due to the existence of noise
and intensity inhomogeneity in brain MR images, many
segmentation algorithms suffer from limited accuracy. In this
paper, they assume that the local image data within each
voxel's neighborhood satisfy the Gaussian mixture model
(GMM), and thus propose the fuzzy local GMM (FLGMM)
algorithm for automated brain MR image segmentation. This
algorithm estimates the segmentation result that maximizes
the posterior probability by minimizing an objective energy
function, in which a truncated Gaussian kernel function is
used to impose the spatial constraint and fuzzy memberships
are employed to balance the contribution of each GMM. They
compared their algorithm to state-of-the-art segmentation
approaches in both synthetic and clinical data. Our results
show that the proposed algorithm can largely overcome the
difficulties raised by noise, low contrast, and bias field, and
substantially improve the accuracy of brain MR image
segmentation.
Hongwei Ji et al, in "ACM-Based Automatic Liver
Segmentation From 3-D CT Images by Combining
Multiple Atlases and Improved Mean-Shift Techniques"
2013 [11], the authors describe In this paper, they present an
autocontext model (ACM)-based automatic liver segmentation
algorithm, which combines ACM, multiatlases, and meanshift techniques to segment liver from 3-D CT images. Our
algorithm is a learning-based method and can be divided into
two stages. At the first stage, i.e., the training stage, ACM is
performed to learn a sequence of classifiers in each atlas space
(based on each atlas and other aligned atlases). With the use of
multiple atlases, multiple sequences of ACM-based classifiers
are obtained. At the second stage, i.e., the segmentation stage,
the test image will be segmented in each atlas space by
applying each sequence of ACM-based classifiers. The final
segmentation result will be obtained by fusing segmentation
results from all atlas spaces via a multi-classifier fusion
technique. Specially, in order to speed up segmentation, given
a test image, they first use an improved mean-shift algorithm
to perform oversegmentation and then implement the regionbased image labeling instead of the original inefficient pixelbased image labeling. The proposed method is evaluated on
the datasets of MICCAI 2007 liver segmentation challenge.
The experimental results show that the average volume
overlap error and the average surface distance achieved by
their method are 8.3% and 1.5 m, respectively, which are
comparable to the results reported in the existing state-of-the-
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art work on liver segmentation.
Kavitha, A.R. et al, in "An efficient approach for brain
tumour detection based on modified region growing and
neural network in MRI images" 2012 [12], the authors
describe Region growing is an important application of image
segmentation in medical research for detection of tumour. In
this paper, they propose an effective modified region growing
technique for detection of brain tumour. Modified region
growing includes an orientation constraint in addition to the
normal intensity constrain. The performance of the proposed
technique is systematically evaluated using the MRI brain
images received from the public sources. For validating the
effectiveness of the modified region growing, the quantity rate
parameter has been considered. For the evaluation of the
proposed technique of tumor detection, the sensitivity,
specificity and accuracy values were used. Comparative
analyses were made for the normal and the modified region
growing using both the Feed Forward Neural Network
(FFNN) and Radial Basis Function (RBF) neural network. The
results show that the modified region growing achieved better
results when compared to the normal technique.
Uzunbas, M.G. et al, in "Coupled Nonparametric Shape
and Moment-Based Intershape Pose Priors for Multiple
Basal Ganglia Structure Segmentation" 2010 [13], the
authors describe This paper presents a new active contourbased, statistical method for simultaneous volumetric
segmentation of multiple subcortical structures in the brain. In
biological tissues, such as the human brain, neighboring
structures exhibit co-dependencies which can aid in
segmentation, if properly analyzed and modeled. Motivated by
this observation, they formulate the segmentation problem as a
maximum a posteriori estimation problem, in which they
incorporate statistical prior models on the shapes and
intershape (relative) poses of the structures of interest. This
provides a principled mechanism to bring high level
information about the shapes and the relationships of
anatomical structures into the segmentation problem. For
learning the prior densities they use a nonparametric
multivariate kernel density estimation framework. They
combine these priors with data in a variational framework and
develop an active contour-based iterative segmentation
algorithm. They test their method on the problem of
volumetric segmentation of basal ganglia structures in
magnetic resonance images. They present a set of 2-D and 3-D
experiments as well as a quantitative performance analysis. In
addition, they perform a comparison to several existent
segmentation methods and demonstrate the improvements
provided by their approach in terms of segmentation accuracy.
Miranda, P.A.V. et al, in "Oriented Image Foresting
Transform Segmentation by Seed Competition" 2014 [14],
the authors describe Seed-based methods for region-based
image segmentation are known to provide satisfactory results
for several applications, being usually easy to extend to
multidimensional images. However, while boundary-based
methods like live wire can easily incorporate a preferred
boundary orientation, region-based methods are usually
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
conceived for undirected graphs, and do not resolve well
between boundaries with opposite orientations. This motivated
researchers to investigate extensions for some region-based
frameworks, seeking to better solve oriented transitions. In
this same spirit, they discuss how to incorporate this
orientation information in a region-based approach called by
exploring digraphs. They give direct proof for the optimality
of the proposed extensions in terms of energy functions
associated with the cuts. To stress these theoretical results,
they also present an experimental evaluation that shows the
obtained gains in accuracy for some 2D and 3D data sets of
medical images.
Greenspan, H. et al, in "Constrained Gaussian mixture
model framework for automatic segmentation of MR
brain images" 2006 [15], the authors describe An automated
algorithm for tissue segmentation of noisy, low-contrast
magnetic resonance (MR) images of the brain is presented. A
mixture model composed of a large number of Gaussians is
used to represent the brain image. Each tissue is represented
by a large number of Gaussian components to capture the
complex tissue spatial layout. The intensity of a tissue is
considered a global feature and is incorporated into the model
through tying of all the related Gaussian parameters. The
expectation-maximization (EM) algorithm is utilized to learn
the parameter-tied, constrained Gaussian mixture model. An
elaborate initialization scheme is suggested to link the set of
Gaussians per tissue type, such that each Gaussian in the set
has similar intensity characteristics with minimal overlapping
spatial supports. Segmentation of the brain image is achieved
by the affiliation of each voxel to the component of the model
that maximized the a posteriori probability. The presented
algorithm is used to segment three-dimensional, T1-weighted,
simulated and real MR images of the brain into three different
tissues, under varying noise conditions. Results are compared
with state-of-the-art algorithms in the literature. The algorithm
does not use an atlas for initialization or parameter learning.
Registration processes are therefore not required and the
applicability of the framework can be extended to diseased
brains and neonatal brains
Maulik, U. in "Medical Image Segmentation Using Genetic
Algorithms" 2009 [16],
the authors describe Genetic
algorithms (GAs) have been found to be effective in the
domain of medical image segmentation, since the problem can
often be mapped to one of search in a complex and
multimodal landscape. The challenges in medical image
segmentation arise due to poor image contrast and artifacts
that result in missing or diffuse organ/tissue boundaries. The
resulting search space is therefore often noisy with a multitude
of local optima. Not only does the genetic algorithmic
framework prove to be effective in coming out of local
optima, it also brings considerable flexibility into the
segmentation procedure. In this paper, an attempt has been
made to review the major applications of GAs to the domain
of medical image segmentation.
Zhao Zaixin et al, in "Neighbourhood weighted fuzzy cmeans clustering algorithm for image segmentation" 2014
ISSN: 2231-5381
[17], the authors describe Fuzzy c-means (FCM) clustering
algorithm has been widely used in image segmentation. In this
study, a modified FCM algorithm is presented by utilising
local contextual information and structure information. The
authors first establish a novel similarity measure model based
on image patches and local statistics, and then define the
neighbourhood-weighted distance to replace the Euclidean
distance in the objective function of FCM. Validation studies
are performed on synthetic and real-world images with
different noises, as well as magnetic resonance brain images.
Experimental results show that the proposed method is very
robust to noise and other image artefacts.
Mayer, A. et al, in "An Adaptive Mean-Shift Framework
for MRI Brain Segmentation" 2009 [18], the authors
describe An automated scheme for magnetic resonance
imaging (MRI) brain segmentation is proposed. An adaptive
mean-shift methodology is utilized in order to classify brain
voxels into one of three main tissue types: gray matter, white
matter, and cerebro-spinal fluid. The MRI image space is
represented by a high-dimensional feature space that includes
multimodal intensity features as well as spatial features. An
adaptive mean-shift algorithm clusters the joint spatialintensity feature space, thus extracting a representative set of
high-density points within the feature space, otherwise known
as modes. Tissue segmentation is obtained by a follow-up
phase of intensity-based mode clustering into the three tissue
categories. By its nonparametric nature, adaptive mean-shift
can deal successfully with nonconvex clusters and produce
convergence modes that are better candidates for intensity
based classification than the initial voxels. The proposed
method is validated on 3-D single and multimodal datasets, for
both simulated and real MRI data. It is shown to perform well
in comparison to other state-of-the-art methods without the
use of a preregistered statistical brain atlas.
Akram, M.U. et al, in "Computer aided system for brain
tumor detection and segmentation" 2011 [19], the authors
describe Magnetic resonance (MR) images are a very useful
tool to detect the tumor growth in brain but precise brain
image segmentation is a difficult and time consuming process.
In this paper they propose a method for automatic brain tumor
diagnostic system from MR images. The system consists of
three stages to detect and segment a brain tumor. In the first
stage, MR image of brain is acquired and preprocessing is
done to remove the noise and to sharpen the image. In the
second stage, global threshold segmentation is done on the
sharpened image to segment the brain tumor. In the third
stage, the segmented image is post processed by
morphological operations and tumor masking in order to
remove the false segmented pixels. Results and experiments
show that their propose technique accurately identifies and
segments the brain tumor in MR images.
Hussain, S.J. et al, in "Segmentation of brain MRI with
statistical and 2D wavelet features by using neural
networks" 2011 [20], the authors describe In this paper, an
efficient technique is proposed for the precise segmentation of
normal and pathological tissues in the MRI brain images. The
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
proposed segmentation technique initially performs
classification process by utilizing FFBNN. Dual FFBNN
networks are used in the classification process. The inputs for
these networks are the features that are extracted in two ways
from the MRI brain images. Five features are extracted from
the MRI images: they are two dynamic statistical features and
three 2D wavelet decomposition features. In Segmentation,
the normal tissues such as WM (White Matter), GM (Gray
Matter) and CSF (Cerebrospinal Fluid) are segmented from
the normal MRI images and pathological tissues such as
Edema and Tumor are segmented from the abnormal images.
The non-cortical tissues in the normal images are removed by
the preprocessing stage. The implementation result shows the
efficiency of proposed tissue segmentation technique in
segmenting the tissues accurately from the MRI images. The
performance of the segmentation technique is evaluated by
performance measures such as accuracy, specificity and
sensitivity. The performance of segmentation process is
analyzed using a defined set of MRI brain.
Long Chen et al, in "Multiple kernel fuzzy C-means based
image segmentation" 2010 [21], the authors describe In this
paper, multiple kernel fuzzy c-means is introduced as a
general framework for image segmentation problem. Multiple
kernel fuzzy c-means provides them a new approach to
combine different information of image pixels in segmentation
algorithms. That is, different information of image pixels are
combined in the kernel space by combining different kernel
functions defined on specific information domains. Two new
segmentation algorithms are derived from the proposed
framework. Simulations on the segmentation of synthetic and
medical images demonstrate the flexibility and advantages of
multiple kernel fuzzy c-means based approaches.
Feng Shi et al, in "Cortical enhanced tissue segmentation
of neonatal brain MR images acquired by a dedicated
phased array coil" 2009 [22], the authors describe The
acquisition of high quality MR images of neonatal brains is
largely hampered by their characteristically small head size
and low tissue contrast. As a result, subsequent image
processing and analysis, especially for brain tissue
segmentation, are often hindered. To overcome this problem, a
dedicated phased array neonatal head coil is utilized to
improve MR image quality by effectively combing images
obtained from 8 coil elements without lengthening data
acquisition time. In addition, a subject-specific atlas based
tissue segmentation algorithm is specifically developed for the
delineation of fine structures in the acquired neonatal brain
MR images. The proposed tissue segmentation method first
enhances the sheet-like cortical gray matter (GM) structures in
neonatal images with a Hessian filter for generation of cortical
GM prior. Then, the prior is combined with their neonatal
population atlas to form a cortical enhanced hybrid atlas,
which they refer to as the subject-specific atlas. Various
experiments are conducted to compare the proposed method
with manual segmentation results, as well as with additional
two population atlas based segmentation methods. Results
show that the proposed method is capable of segmenting the
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neonatal brain with the highest accuracy, compared to other
two methods.
Dubey, R.B. et al, in "Evaluation of Three Methods for
MRI Brain Tumor Segmentation" 2011 [23], the authors
describe Imaging plays a central role in the diagnosis and
treatment planning of brain tumor. An accurate segmentation
is critical, especially when the tumor morphological changes
remain subtle, irregular and difficult to assess by clinical
examination. Traditionally, segmentation is performed
manually in clinical environment that is operator dependent
and very tedious and time consuming labor intensive work.
However, automated tumor segmentation in MRI brain tumor
poses many challenges with regard to characteristics of an
image. A comparison of three different semi-automated
methods, viz., modified gradient magnitude region growing
technique (MGRRGT), level set and a marker controlled
watershed method is undertaken here for evaluating their
relative performance in the segmentation of tumor. A study on
9 samples using MGRRGT reveals that all the errors are
within 6 to 23% in comparison to other two methods.
Zhuowen Tu et al, in "Auto-Context and Its Application to
High-Level Vision Tasks and 3D Brain Image
Segmentation" 2010 [24], the authors describe The notion of
using context information for solving high-level vision and
medical image segmentation problems has been increasingly
realized in the field. However, how to learn an effective and
efficient context model, together with an image appearance
model, remains mostly unknown. The current literature using
Markov Random Fields (MRFs) and Conditional Random
Fields (CRFs) often involves specific algorithm design in
which the modeling and computing stages are studied in
isolation. In this paper, they propose a learning algorithm,
auto-context. Given a set of training images and their
corresponding label maps, they first learn a classifier on local
image patches. The discriminative probability (or
classification confidence) maps created by the learned
classifier are then used as context information, in addition to
the original image patches, to train a new classifier. The
algorithm then iterates until convergence. Auto-context
integrates low-level and context information by fusing a large
number of low-level appearance features with context and
implicit shape information. The resulting discriminative
algorithm is general and easy to implement. Under nearly the
same parameter settings in training, they apply the algorithm
to
three
challenging
vision
applications:
foreground/background
segregation,
human
body
configuration estimation, and scene region labeling.
Moreover, context also plays a very important role in
medical/brain images where the anatomical structures are
mostly constrained to relatively fixed positions. With only
some slight changes resulting from using 3D instead of 2D
features, the auto-context algorithm applied to brain MRI
image segmentation is shown to outperform state-of-the-art
algorithms specifically designed for this domain. Furthermore,
the scope of the proposed algorithm goes beyond image
analysis and it has the potential to be used for a wide variety
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
of problems for structured prediction problems.
II.
MRI BRAIN SEGMENTATION USING SVM
Segmentation of health imagery is a challenging setback
due to the intricacy of the pictures, as well as to the
nonexistence of models of the anatomy that fully arrest the
probable deformations in every single structure. The mind is a
chiefly convoluted construction, and its segmentation is an
vital pace for countless setbacks, encompassing studies in
temporal change detection of morphology, and 3-D
visualizations for surgical planning. Pursuing fig displays the
Mind picture of the patient.
Figure 3 Lesions Marked by the Expert for Patient 1
Fig below Shows the training data set from this patient, Make
a matrix with the training data feature vectors from the lesion
pixels, the feature vectors are local intensity patches.
Training set Patient 1
MRI T1
100
100
200
200
300
300
400
400
500
500
100
Figure 2 Original Brain Scan of Patient 1 showing
complete brain
Diseases indulged in the Center contain both benign and
malignant tumors of the mind, spinal cord, skull center, and
spine in adults and children. Targeted pathologies contain
main
parenchymal
tumors
(including
gliomas,
craniopharyngiomas, germ cell tumors, and lymphomas),
metastatic tumors (including parenchymal, leptomenngeal,
and osseous lesions) and tumors of the skull center (including
meningiomas, osseous and cartilagenous tumors, pituitary
adenomas, aural neuromas and glomus tumors). Selected
Lesions are shown in fig below by an Expert surgeon.
ISSN: 2231-5381
200
300
400
500
100
200
300
400
500
100
200
300
400
500
MRI T2
100
100
200
200
300
300
400
400
500
500
100
200
300
400
500
Figure 4 Training Data of the Patient Showing T1 and T2
Images with Marked Regions of the Brain
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 7- May 2015
Training set patient 1
SVM with C = 1.00
MRI T1
1
100
0.9
200
200
0.8
300
300
400
400
500
500
100
200
300
400
500
100
MRI T2
200
300
400
True detection rate
100
500
Tumours marked by Experts
0.7
0.6
0.5
0.4
0.3
0.2
0.1
100
Proposed SVM
LINEAR SVM
100
0
200
200
300
300
400
400
500
0
200
300
400
500
100
200
300
400
500
Figure 5 Test Data Showing T1 and T2 Images with
Tumors of the Brain
Test set using Patient 3
MRI T1
100
100
200
200
300
300
400
400
500
500
100 200 300 400 500
MRI T2
100 200 300 400 500
Tumour Regions classified by the algorithm
100
100
200
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300
400
400
500
500
100 200 300 400 500
100 200 300 400 500
In our method for segmentation of mind tissue from magnetic
resonance pictures, we join methods that use grey level,
topological and spatial data in the images. The specific
methods we use are: Prop vector segmentation for an
intensity-based correction and association of the data, binary
morphology and connectivity for combination of comparative
topological data, and balloon-based deformable contours for
supplement of spatial data to the segmentation procedure.
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 6 The Results of the SVM based Segmentation and
Classification process comparing Linear SVM and
Proposed SVM Method, showing on average 88.96%
accuracy
III.
RESULTS ANALYSIS
One of the challenging tasks in the health span is brain
tumor segmentation that consists on the extraction procedure of
tumor spans from images. Generally, this task is completed
manually by health specialists that is not always seeming due
to the similarity amid tumor and normal tissues and the
elevated diversity in tumors appearance. Thus, automating
health picture segmentation stays a real trial that has enticed
the attention of countless researchers in last years. In this work,
we have concentrated on segmentation of Magnetic Resonance
mind Pictures (MRI). Our believed is to ponder this setback as
a association setback whereas the target is to discriminate amid
normal and atypical pixels on the basis of countless features,
namely intensities and texture. Extra precisely, we worked to
use Prop Vector Contraption (SVM) that is inside accepted and
well inspiring association methods and attained 88.96%
accuracy.
IV.
[1].
[2].
[3].
ISSN: 2231-5381
0.2
False alarm rate
500
100
0.1
REFERENCES
Gholipour, A.; Kehtarnavaz, N.; Briggs, R.; Devous, M.; Gopinath,
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Registration of Heterogeneous Populations of Brain MR
Images",IEEE,Medical Imaging, IEEE Transactions on,2014
Shu Liao; Yaozong Gao; Jun Lian; Dinggang Shen,"Sparse PatchBased Label Propagation for Accurate Prostate Localization in CT
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