International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
Medullablastoma Neoplasm in MR
Images Dissection on FCM Method
D.Satheesh Kumar#1, Dr. K.P.Yadav*2
#
Research Scholar, Department of Computer Science and Engineering,
SunRise University, Alwar, India.
*
Professor & Director, Mangalmay Institute of Engineering & Technology ,
Greater Noida,U.P, India.
Abstract— A system that automatically segments and labels
Medullablastoma Neoplasm tumors in magnetic resonance
images (MRI’s) of the human brain is presented. The MRI’s
consist of T1-weighted, proton density, and T2-weighted
feature images and are processed The brain magnetic
resonance (MR) image of the Medullablastoma Neoplasm for
lateral ventricular deformation feature extraction component
consists of three streamlined processes of retrieving lateral
ventricular shape, estimating lateral ventricular deformation,
and transforming lateral ventricular deformation to feature.
These processes are used to obtain the lateral ventricular
boundary by extracting lateral ventricles, estimate
deformation by measuring the geometrical variation between
aligned template and target lateral ventricles, and convert the
estimated lateral ventricular deformation data into feature for
segmentation. Following the component architecture, the
implementation details of these processes are presented in this
chapter, in particular, methods and algorithms for each of the
processes are explained. To validate the applicability of the
proposed methods, extensive experiments are performed and
the results are evaluated and discussed.
diagnosed each year in the UK. Many of these are
malignant. Medulloblastomas are more common in
children, particularly between the ages of three and eight.
Keywords—: Fuzzy C- Means, Brain Tumor, Surgery
Fig: 1.1 Medullablastoma Neoplasm
simulation, Image Registration, MRI Segmentation
I.
INTRODUCTION
With the progress of medical imaging and computer
technologies, much research effort has been devoted to
develop methods for task-specific imaging and computeraided detection and diagnosis (CAD). Medulloblastoma is a
highly malignant primary brain tumor that originates in the
cerebellum or posterior fossa. Tumors that originate in the
cerebellum are referred to as infratentorial because they
occur below the tentorium, a thick membrane that separates
the cerebral hemispheres of the brain from the cerebellum.
Another term for medulloblastoma is infratentorial
primitive neuroectodermal tumor (PNET). Very rarely,
medulloblastomas may spread to other parts of the body. If
they do spread to other parts of the brain or to the spinal
cord, this is usually through the cerebrospinal fluid (CSF).
CSF is the fluid that surrounds and protects the brain and
the spinal cord. Tumours affecting the central nervous
system (CNS), which is made up of the brain and spinal
cord, are fairly rare. About 4,500 new tumours are
ISSN: 2231-5381
They make up about 1 in 5 (20%) of all childhood brain
tumours. The tumour is more common in boys than girls.
They rarely occur in adults Medulloblastoma is the most
common PNET originating in the brain.[1] All PNET
tumors of the brain are invasive and rapidly growing tumors
that, unlike most brain tumors, spread through the
cerebrospinal fluid (CSF) and frequently metastasize to
different locations in the brain and spine. The tumor is
distinctive on T1 and T2-weighted MRI with heterogeneous
enhancement and typical location adjacent to and extension
into the fourth ventricle. Histologically, the tumor is solid,
pink-gray in color, and is well circumscribed. The tumor is
very cellular, many mitoses, little cytoplasm, and has the
tendency to form clusters and rosettes. Correct diagnosis of
medulloblastoma may require ruling out atypical teratoid
rhabdoid tumor (ATRT)[17]
This image processing consist of image enhancement using
histogram equalization, edge detection and segmentation
process to take patterns of brain tumors, so the process of
making computer aided diagnosis for brain tumor grading
http://www.ijettjournal.org
Page 84
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
will be easier. The terminology used to define, describe and
categorize vascular anomalies, abnormal lumps made up of
blood vessels, has changed. The term hemangioma was
originally used to describe any vascular tumor-like
structure, whether it was present at or around birth or
appeared later in life.
Fig: 1.2 Pathaology of Hemangioma affected cells
Brain tumors an anomalous intracranial engorgement
instigated by cells reproducing themselves in an
unrepressed manner. In the United States alone, there were
proximate of 16,500 new cases of brain neoplasm in the
year 2000, which chronicled for 1.4 proportions of all
cancers, 2.4 proportions of all cancer deaths, and 20 to 25
proportions of paediatric cancers. It was anticipated that
there are crudely 13,000 deaths per year as a upshot of brain
tumors [Greenlee et al., 2000]. Magnetic resonance imaging
(MRI) is apprehensive as a powerful conception technique
that countenances images of pulp anatomy to be espoused in
a dwindling and non- hampering manner. It stipulates much
prodigious contrast amid disparate malleable tissues of the
body than computed tomography (CT), hewing it
predominantly
beneficial
in
abetting
diagnosis,
unambiguously in gauging brain tumors. Automatic brain
tumor dissection in magnetic resonance (MR) image is a
momentous image exemption step for both medical
practitioners and scientific researchers. For illustrate, it can
be pragmatic in assessing tumor escalation moreover
treatment ripostes, augmenting computer-assisted surgery,
intending radiation therapy, and instigating tumor polyp
models. Within inured MR image dissection systems, two
of the most indispensable onuses are feature extraction and
segmentation. Feature extraction propounds the gradation
data on which the dissection constituent consigns. With the
traits that are extricated from the images, such as MRI
intensities and other premeditated features, e.g., edge,
texture, the dissection component then contrives
ISSN: 2231-5381
estrangement of neoplasm and non-necrosis tissue regions.
Large expanses of research ventures have been rendered in
inchoate dexterous segmentation methods in the antique
years, however, such modus have botched to apprehend the
exactitude level analogous to visual scrutinizes instigated by
human connoisseurs. In addition, brain tumor pretentious
MR images comprehend many physiognomies that oblige
mundane tactics used in automated systems erroneous. One
of the most rebellious impediments that thwart the
amendment of ingenuous automatic systems is that MR
image paucities strong connotation between tangible
anatomical presaging and MRI intensity, remarkably for
brain tumor. Subsequently, in addition to MRI intensities,
other reckoned features which are pertinent to anatomical
insinuating are entailing for the brain tumor dissection
tasks. One such countenance crammed in this research is the
brain tangential ventricular sabotage. It can be lucidly
pragmatic that in poised MR image cases, although
tangential ventricles may be sporadic in size, shape and
position, they will be abridged when cerebral tumor or
edema is pliable. In most cases, retrenchment within the
crosswise ventricular precinct is in a penchant contrary to
that of the tumor progression. To cognize the accord
between brain tumor swelling and brain lateral ventricular
caricature, some rudimentary demands that need to be
answered are:

Is it conceivable to foster methods to contemplate
distortion of brain crosswise ventricles?

Can rateable connections between blemish of
crosswise ventricles and the obstinacy of brain
neoplasms be obtained?

Can the results assimilated through gauging lateral
ventricular distortion be used for brain tumor
dissection?

How far can the corollaries of automated neoplasm
dissection be amended after incorporating lateral
ventricular deformation despatch as supplementary
constraints?

To riposte the above probes, an inkling of
renovating lateral ventricular contort into facet to
be used for tumor dissection is therefore recced in
this journal.

In order to contrivance this proposition,
multidisciplinary revisions of brain lumps,
adjacent
ventricles,
deformation-based
morphometry (DBM), brain MR image
segmentation methods and collaborative MR
http://www.ijettjournal.org
Page 85
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
image segmentation systems along with the feature
extraction constituent have been coxswained.
The crucial empirical of this research is to nurture methods
and etiquette to augment exactitude of brain tumor maiming
in MR images. The prominent research phenomena
unambiguously focus on reconnoitring and enlightening
feature premise of information on the brain lateral
ventricular distortion. This superfluous feature is then used
as one of the assents to the general dissection methods to
convalesce brain tumor segmentation fallouts. Some of the
disputes this research aims to discourse are recapitulated as
follows:
1. Ascertaining major invigorating issues in brain MR
image tumor segmentation;
2. Cramming the tackle of the lateral ventricles and brain
neoplasm from both anatomical and MRI-visualization
facets, and probing the fabrications between the
decisiveness of lenient tissue structures due to the
persistence of brain lesions and prevarication pragmatic in
the divergent ventricles;
3. Ascertaining snags concomitant to the quantitative
magnitude or assessment of lateral ventricular distortion;
4. Recceing suitable methods for renovating lateral
ventricular deformation to trait;
5. Intriguing and instigating a realistic system which is
competent of metamorphosing this deformation into a
rateable feature;
6. Entangling the superfluous feature to tumor dissection
methods and gauging the inclusive meticulousness of the
tumor dissection.
7. Neural networks is pinpointed apposite to do these
remits.
II.
LITERATURE SURVEY
The process of In computer vision, the delegation of
segmentation is to severance a digital image into multiple
splinters [Gonzalez and Woods, 2002]. The entreaty of
segmentation is to streamline and/or change the portrayal of
an image to be more evocative and affluent to scrutinize.
More austerely, image segmentation is the process of dole
out a label to every solitary pixel in an image such that
pixels with the staunch label apportion compatible with
visual physiognomies and luminary [Shapiro and Stockman,
2001]. The upshots of image dissection is a set of
fragments, regions or silhouettes disentangled from the
image. Pixels within one region are counterpart to each
ancillary in terms of some temperaments or computed
ISSN: 2231-5381
accredits, such as colour, grey scale, contrast or texture.
Flanking regions are drastically idiosyncratic with esteem to
the same moralities [Shapiro and Stockman, 2001].
Crumbling is one of the prerequisite rungs within the
analysis and processing of medical images. The primary
objective of medical image segmentation methods is to aid
diagnosis process. In other words, these methods are used to
assist medical practitioners in evaluating medical imagery
or recognising abnormal findings in a medical image [Suri
et al., 2002]. Accurate medical image segmentation is useful
in both clinical and scientific applications which include
enhanced visualisations, high-throughput and consistent
volume measurements, research into structural shape and
variations, image-guided surgery, and change detection in
images acquired at different times [O’Donnell, 2001]. In
medical image segmentation, structures of interest include
organs, parts of pathology, abnormalities (such as brain
tumour) and normal structures. This has led to the
development of a wide range of segmentation methods
addressing specific problems in medical applications. Some
methods proposed in the literature are extensions of
methods originally proposed for generic image
segmentation [Suri et al., 2002]. In contrast to generic
segmentation, methods for medical image segmentation are
often application-specific; as such, they often utilise prior
knowledge about particular objects of interest and other
structures in the image [Shapiro and Stockman, 2001; Suri
et al., 2002]. Previous work on medical image segmentation
can be roughly divided into two broad categories: single
medical image segmentation, where only one image is
employed as input data; and multi-spectral images such as
MR images where different modalities are available [Clarke
et al., 1995]. While the focus is placed on the recent
methods and applications used in MR image segmentation,
some early literature on medical image segmentation are
reviewed. Methods for single medical image segmentation
can be further divided into four general sub-categories:
thresholding-based, edge-based, seed growing, and those
that fall outside these previous sub-categories [Clarke et al.,
1995]. Thresholding-based is the simplest and the most
intuitive single image segmentation approach, where one or
more criteria is usually based on image contrast selected as
thresholds in order to arrange the image pixels into one or
more categories [Gordon et al., 1996; Sahoo et al., 1988].
However clinical application of thresholding-based
segmentation methods is severely hindered if the image
contrast value is not strongly associated with the anatomical
meaning in medical images [Prastawa et al., 2005]. Edgebased segmentation methods take advantage of edge
detection to delineate regions, but they often suffer from
incorrect detection of edges due to noise, over- and undersegmentation, and variability in threshold selection in the
image edge [Dellepiane, 1991]. Although improved results
have been obtained through a combination of edgedetection (the Marr-Hildreth operator) with morphological
filtering [Bomans et al., 1990], boundary tracing [Ashtari et
al., 1990] and employing neighbourhood information
[Singleton et al., 1997], the complexity observed in medical
images, especially when pathology exists, makes precise
http://www.ijettjournal.org
Page 86
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
detection of boundaries between two structures rather
challenging. Seed growing segmentation methods require an
operator to empirically select seeds and thresholds first.
Pixels around the seeds are examined and included in the
region if they fall within pre-determined thresholds, and
each of the added pixels then becomes a new seed for the
following iteration. The process is continued until the image
is completely segmented [Cline et al., 1987]. Results
obtained through seed growing segmentation techniques are
dependent on the selected operator settings for the seeds
[Pohlman et al., 1996]. A few of the other single image
segmentation methods include statistical models such as
Markov random field (MRF) methods [Dubes and Jam,
1989]. Although segmentation results can be improved by
applying these methods, their practical implementation is
limited due to the difficulties in selecting parameters or
defining specific functions. More recent research has
demonstrated that segmentation results can be improved by
combining some of the abovementioned methods with other
image processing approaches [Gibbs et al., 1996; Zhu and
Yan, 1997]. It can be summarised that although single
image segmentation methods may be applicable in some
medical image cases, their effectiveness is generally limited
to relatively simple structures or pathology due to the fact
that the information provided by a single image may not be
enough for complex applications. For more challenging
medical image segmentation tasks, limiting input data to
that of single images generally fail to obtain robust results
[Clarke et al., 1995].
volume measurements, research into structural shape and
variations, image-guided surgery, and change detection in
images acquired at different times [O’Donnell, 2001]. In
medical image segmentation, structures of interest include
organs, parts of pathology, abnormalities (such as brain
tumour) and normal structures. This has led to the
development of a wide range of segmentation methods
addressing specific problems in medical applications. Some
methods proposed in the literature are extensions of
methods originally proposed for generic image
segmentation [Suri et al., 2002]. In contrast to generic
segmentation, methods for medical image segmentation are
often application-specific; as such, they often utilise prior
knowledge about particular objects of interest and other
structures in the image [Shapiro and Stockman, 2001; Suri
et al., 2002]. Previous work on medical image segmentation
can be roughly divided into two broad categories: single
medical image segmentation, where only one image is
employed as input data; and multi-spectral images such as
MR images where different modalities are available [Clarke
et al., 1995]. While the focus is placed on the recent
methods and applications used in MR image segmentation,
some early literature on medical image segm ntation are
reviewed. framework integrates edges and region-based
segmentation with spectral-based clustering as depicted in
Figure 3.1.
III. METHODOLOGY
In computer vision, the delegation of segmentation is to
severance a digital image into multiple splinters [Gonzalez
and Woods, 2002]. The entreaty of segmentation is to
streamline and/or change the portrayal of an image to be
more evocative and affluent to scrutinize. More austerely,
image segmentation is the process of dole out a label to
every solitary pixel in an image such that pixels with the
staunch label apportion compatible with visual
physiognomies and luminary [Shapiro and Stockman,
2001]. The upshots of image dissection are a set of
fragments, regions or silhouettes disentangled from the
image. Pixels within one region are counterpart to each
ancillary in terms of some temperaments or computed
accredits, such as colour, grey scale, contrast or texture.
Flanking regions are drastically idiosyncratic with esteem to
the same moralities [Shapiro and Stockman, 2001].
Crumbling is one of the prerequisite rungs within the
analysis and processing of medical images. The primary
objective of medical image segmentation methods is to aid
diagnosis process. In other words, these methods are used to
assist medical practitioners in evaluating medical imagery
or recognising abnormal findings in a medical image [Suri
et al., 2002]. Accurate medical image segmentation is useful
in both clinical and scientific applications which include
enhanced visualisations, high-throughput and consistent
ISSN: 2231-5381
Fig: 3.1 Block diagramof the proposed method (WNCUT).
IV.
DISSECTION ON FCM METHOD
FCM clustering method is suitable for the task of brain
tissue segmentation because it supports multi-dimensional
feature data, therefore, the rich information contained in
multi-spectral MR images can be well utilised. However
this method has some disadvantages when employed for
http://www.ijettjournal.org
Page 87
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
brain MR image tissue segmentation, such as sensitivity to
noise and incapability to vary relative importance
(adjustable weights) of different features [Pedrycz and
Waletzky, obtain the centroid and the mean value of the
new region. 1997; Yeung and Wang, 2002; Bargiela et al.
2004; Chuang et al., 2006; Hung et al. 2008].
near to the centroid of this cluster, and low membership
value means this pixel is far from the centroid of this
particular cluster
∑
(
)
List of Modules:
1.
Conventional FCM Algorithm
2.
FCM Method with Gaussian Smoothing
3.
Feature Weights Adjustable FCM Method
4.
Neoplasm Lateral Ventricles Extraction
. In the FCM algorithm, the probability is dependent solely
on the distance between each point and individual cluster
centroid in the feature domain [Bezdek et al., 1993]. The
membership function and cluster centroids are iteratively
updated based on Equations.
∑
Conventional FCM Algorithm
∑
FCM is a widely used clustering method that groups data
elements to two or more clusters, associating with each
element a set of membership levels. These membership
levels indicate the strength of association between that data
element and a particular cluster. Fuzzy clustering is a
process of assigning these membership levels, and then
using them to assign data elements to desired clusters
[Bezdek, 1980].
Let an image be represented as ( , , , , ) 1 2 3 N X x x x L x ,
where x represents individual pixel within multidimensional (multiple features6) data and N denotes the
number of pixels. The algorithm works through an iterative
optimization procedure to minimise the cost function which
is defined as follows:
∑∑
where ij u represents the degree of membership of data
elements j x in the ith cluster, i v is the centroid of the ith
cluster, c is the number of clusters to be partitioned, * is a
norm (Euclidean distance measures are commonly applied)
representing the distance which expresses the similarity
between measured multi-feature data and the cluster
centroid, and m is a real constant greater than which
controls the fuzziness of the resulting partition [Bezdek et
al., 1993]. Through updating cluster centroids iteratively,
the cost function can be minimised as the summation of
distances between image pixels to the centroid of clusters
decreases. By indicating the closeness between pixels and
cluster centroids, the membership function represents the
probability that a pixel belongs to a specific cluster. A high
membership value with a cluster suggests that a pixel is
ISSN: 2231-5381
The iteration will stop when {J J } σ p p − < +1 max , where
σ is the termination criterion between 0 and 1, and p is the
iteration step.
FCM Method with Gaussian Smoothing
With regards to the previous discussions about
disadvantages and related works on improving conventional
FCM for MR image segmentation, this section proposes an
enhanced FCM method termed “feature weights adjustable
FCM (fwaFCM)” with Gaussian smoothing. Techniques of
incorporating Gaussian smoothing, the fwaFCM method
and automatic feature weight selection for fwaFCM are
introduced. These techniques attempt to reduce errors
caused by noise, enable feature weights to be adjustable,
and select optimised feature weights.
Feature Weights Adjustable FCM Method
As discussed earlier in this section, another problem in the
conventional FCM is that, conventional FCM algorithm
employs the standard Euclidean distance function, hence
assigns the same and non-adjustable weight to all features,
although it supports multi-dimensional feature set. By
expressing j i x − v with Euclidean distance function ij D ,
as in the standard FCM,
∑∑
In the measurement of input feature set using standard
Euclidean distance function, weights of all features in the
http://www.ijettjournal.org
Page 88
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
input feature set are exactly the same. As a result, the
conventional FCM algorithm is not able to provide finetuning of the clustering results by changing feature weights.
This becomes problematic when one or more input features
need to be emphasised and other features to be
deemphasised. To adjust the weights of each input feature,
Equation can be modified by adding factors αk, hence it
becomes:
√∑(
)
where k α is the weighting factor for the k-th feature.
Introducing k α makes weight of each input feature
adjustable.
Neoplasm Lateral Ventricles Extraction
The original healthy MR images and their corresponding
extracted lateral ventricles as general masks for extracting
lateral ventricles in tumour-affected MR images. After the
lateral ventricles are extracted, they are slightly enlarged to
the extent which allows lateral ventricles used in this
research to be covered. There are several factors that make
human intervention necessary in the process of lateral
ventricles extraction. One of these factors can be
generalized as the result of the wrongly clustered CSF tissue
pixels which is caused by the employed brain tissue
segmentation method. This is primarily due to the fact that
the proposed brain tissue segmentation step using clustering
method may wrongly include image pixels which are
difficult to be distinguished from other tissue types lateral
ventricles alignment and lateral ventricular deformation
measurement. Lateral ventricles alignment is achieved by
two sub-steps of lateral ventricles linking and landmark
points selecting. The lateral ventricular deformation
measurement step is also achieved by two sub-steps of
modelling lateral ventricular deformation and calculating
estimated deformation. In this section, firstly the related
work on selecting deformation modelling function is
introduced. Misalignment caused by the imperfect template
lateral ventricles is then discussed, which initiates the need
for lateral ventricular deformation adjustment.
identified manually out from 6 clusters after the clustering
process due to the fact that there is no training data for the
FCM method. By using input feature set with or without the
extracted deformation feature, pixels segmented as tumor
which are in the same class as the corresponding pixels in
the segmentation by medical expert are treated as correctly
segmented tumor pixels; those segmented as tumor but
labeled as non-tumor in the segmentation by medical expert
are treated as wrongly segmented tumor pixels.The
feasibility of the project is analyzed in this phase and
business proposal is put forth with a very general plan for
the project and some cost estimates. During system analysis
the feasibility study of the proposed system is to be carried
out. By incorporating the relevant lateral ventricular
deformation as an additional feature in the feature set for
pattern recognition segmentation methods, brain tumor
segmentation accuracy increases.requirements for the
system is essential. Statistical measures of sensitivity and
specificity [28], [29] are applied for evaluating the
segmentation results. By treating correctly segmented
tumor, wrongly segmented tumor, correctly segmented nontumor and wrongly segmented nontumor pixel number as
true positive (true+), false positive (f alse+), true negative
(true−) and false negative (f alse−) number respectively,
sensitivity and specificity values can be obtained according
to: Sensitivity = true+ true++false− and Specif icity = true−
true−+false+ , respectively. A sensitivity of 100% means
that the test recognizes all actual positives, i.e., all brain
tumor pixels are segmented as tumor. And a specificity of
100% means that the test recognizes all actual negatives,
i.e., all non-tumor pixels are segmented as nontumor [28].
V. EXPERIMENT RESULT
The each testing image pixel is categorized as tumor or nontumor. In the experiments using unsupervised FCM
algorithm, the number of clusters is set to 6 denoting six
clusters of major brain tissues. The cluster of tumor will be
ISSN: 2231-5381
Fig: 5.1 Neoplasm Extraction on Probability Distributed Function Analysis
http://www.ijettjournal.org
Page 89
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
Fig: 5.5 Phantom Data Measurement in the Iterationa level -2 for Gray
Scale
Fig: 5.2 Phantom Data Measurement in the MR Images in the Initial
Iteration
Fig: 5.6 Phantom Data Measurement in the Iterationa level -3 for Gray
Scale
Fig: 5.3 Phantom Data Measurement in the MR Images in the Final
Iteration
Fig: 5.4 Example of landmark points with original and deformed TPS
meshes formed by x and y coordinates: (a) original; (b) deformed. (c)
Illustration of the effect of deformation
ISSN: 2231-5381
Fig: 5.7 Medullablastoma Neoplasm Extracted Based on FCM with offset
Cluster Means
http://www.ijettjournal.org
Page 90
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
The lateral ventricular deformation feature extraction for
the Medullablastoma is in fact a procedure of transforming
deformation of lateral ventricles into data that can be used
by methods for brain tumour segmentation. Within the
procedure, the three continuous processes of retrieving
lateral ventricular shape, aligning lateral ventricles and
transforming deformation to feature are used to retrieve,
process and measure the lateral ventricular deformation
caused by compression from brain tumours. In the process
of retrieving lateral ventricular shape, a new brain tissue
segmentation scheme is proposed. To address the problems
of conventional FCM algorithm, this segmentation scheme
employs the fwaFCM algorithm with Gaussian smoothing
so as to allow the input feature weights to be adjustable and
hence reduce noise sensitivity of the clustering process.
With this scheme, CSF tissue can be separated and grouped
into one cluster. This cluster of CSF tissue is then selected
by the step of CSF identification based on the properties of
CSF. The lateral ventricles are then extracted by using a
global mask along with human intervention to remove CSF
pixels that are wrongly classified or reside outside of the
lateral ventricular region. In the process of estimating lateral
ventricular deformation, alignment of lateral ventricles is
achieved by automatically linking the disjointed lateral
ventricles, followed by the process of selecting landmark
points to associate the template and target lateral ventricles.
These pairs of points are selected based on using tips of
lateral ventricular anterior and posterior horns as key
landmark points, along with some intermediate landmark
points. With the aligned lateral ventricles, the selected
deformation modeling function can be used for lateral
ventricular deformation measurement. The obtained
deformation data can be then converted to image greyscale
values.It can be seen from the results that, the extracted
lateral ventricular deformation feature generally coincides
with the existence of brain tumour. This suggests the strong
correlation between the extracted lateral ventricular
deformation feature and brain tumour. It is hoped that by
adding the extracted lateral ventricular deformation feature,
brain tumour segmentation accuracy can be enhanced.
REFERENCES
[1] K. Baete, J. Nuyts, W. Van Paesschen, P. Suetens, and P. Dupont,
“Anatomical-based FDG-PET reconstruction for the detection of
hypometabolic regions in epilepsy,” IEEE Trans. Med. Imag., vol.
23, no. 4, pp. 510–519, Mar. 2004.
[2] J. Nuyts, “The use of mutual information and joint entropy for
anatomical priors in emission tomography,” in IEEE Nucl. Sci.
Symp. Conf. Rec., 2007, pp. 4149–4154.
[3] K. Vunckx and J. Nuyts, “Heuristic modification of an anatomical
Markov prior improves its performance,” in IEEE Nucl. Sci. Symp.
Conf. Rec., 2010, pp. 3262–3266.
ISSN: 2231-5381
A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,”
ACM Computing Surveys, vol. 31, issue 3, pp. 264-323, Sep. 1999.
[5] Fabijanska, A.,” A survey of thresholding algorithms on yarn images
“, MEMSTECH, International Conference pp.23-26,2000
[6] P. A. Wingo, T. Tong, and S. Bolden, “Cancer statistics,” A. Cancer
J. Clin., vol. 45, pp. 8–30, 1995.
[7] D. L. Lamm and F. M. Torti, “CA: Bladder cancer,” A. Cancer J.
Clin., vol. 46, pp. 93–112, 1996.
[8] A. Jemal, A. Thomas, T. Murray, and M. Thun, “Cancer statistics,” A
Cancer J. Clin., vol. 52, pp. 23–47, 2002.
[9] Held, Karsten., Kops, Elena Rota., Krause, J. Bernd., Wells, William
M., Kikinis, Ron., Müller-Gärtner, Hans- Wilhelm. (1997). “Markov
Random Field Segmentation of Brain MR Image”. IEEE Trans. Med.
Imag. vol. 16, pp. 878-886.
[10] Leemput, Koen Van., Maes, Frederik., Vandermeulen, Dirk., Suetens,
Paul. (1999). “Automated model-based tissue classification of MR
images of the brain”. IEEE Transaction on Medical Imaging, pp.897908. [11] Marion, A. (1991). An Introduction to Image Processing.
Chapman and Hall, p 274.
[11] Vasuda, p., Satheesh, S. (2010). Improved Fuzzy C-Means Algorithm
for MR Brain Image Segmentation. International Journal on
Computer Science and Engineering (IJCSE).Vol. 02, 05, Page(s):
1713-1715. [13] Pietro, Perona and Malik, Jitendra. (1987). “Scalespace and edge detection using anisotropic diffusion”. Proceedings
of IEEE Computer Society Workshop on Computer Vision, pp. 16–
22.
[12] F. Lefebvre, G. Berger, and P. Laugier, “Automatic Detection of the
Boundary of the Calcaneus from Ultrasound Parametric Images
Using an Active Contour Model-Clinical Assessment,” IEEE
Transactions on Medical Imaging, vol. 17(1), 1998, pp. 45–52.
[13] W. Wang, J.E. Mottershead, and C. Mares, “Modeshape Recognition
and Finite Element Model Updating Using the Zernike Moment
Descriptor,” J. Mechanical Systems and Signal Processing, vol. 23,
2009, pp. 2088-2112.
[14] P. Gibbs, D. Buckley, S. Blackband, and A. Horsman, “Tumour
Volume Determination from MR Images by Morphological
Segmentation,” Physics in Medicine and Biology, vol. 13, 1996, pp.
2437–2446.
[15] J.K. Udupa, P.K. Saha, and R.A. Lotufo, “Relative Fuzzy
Connectedness and Object Definition: Theory, Algorithms, and
Applications in Image Segmentation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 24(11), 2002 pp. 1485–1500.
[16] A. Khotanzad and Y.H. Hong, “Rotation Invariant Image Recognition
Using Features Selected via a Systematic Method,” Pattern
Recognition vol. 23(10) , 1990, pp. 1089-1101.
[17] S. Ghosal and R. Mehrotra, “Robust Optical Flow Estimation Using
Semiinvariant Local Features,” Pattern Recognition, vol. 30(2),
1997, pp. 229-237.
[18] S.X. Liao and M. Pawlak, “On the Accuracy of Zernike Moments for
Image Analysis”, IEEE Trans. Pattern anal. Mach. Intell., vol. 20,
1998, pp. 1358-1364.
[19] R.R. Bailey and M. Srinath, “Orthogonal Moment Features for use
with Parametric and Non-parametric Classifiers,” IEEE Trans.
Pattern anal. Mach. Intell., vol. 18, 1996, pp. 389-400.
[20] A. Khademi, A. Venetsanopoulos, and A. Moody, “Edge-based PVA
estimation in FLAIR MRI with WML,” in Proc. IEEE Eng. Med. Biol.
Soc. Conf., 2010, pp. 6114–6117.
[21] P. H. Kvam and B. Vidakovic, “Nonparametric Statistics with
Applications to Science and Engineering”. New York: WileyInterscience, 2007.
[22] A. Popovic, M. de la Fuente, M. Engelhardt, and K. Radermacher,
“Statistical validation metric for accuracy assessment in medical
image segmentation,” Int. J. Comput. Assist. Radiol. Surg., vol. 2, pp.
169–181, 2007.
[23] C. Lisanti, C. Carlin, K. P. Banks, and D.Wang, “Normal MRI
appearance and motion-related phenomena of CSF,” Amer. J.
Roentgenol., vol. 188, no. 3, pp. 716–725, 2007.
[24] Vijayakumar, B., and Ashish Chaturvedi. "Automatic Brain Tumors
Segmentation of MR Images using Fluid Vector Flow and Support
Vector Machine." Research Journal of Information Technology 4.
[25] Vijayakumar, B., and Ashish Chaturvedi. "Tumor Cut-Segmentation
and Classification of MR Images using Texture Features and Feed
Forward Neural Networks" European Journal of Scientific Research
85.3 (2012): 363-372
[4]
VI.CONCLUSION
http://www.ijettjournal.org
Page 91
International Journal of Engineering Trends and Technology(IJETT) - Volume 4 Issue 1-Jan 2013
[26] Vijayakumar, B., and Ashish Chaturvedi, “Idiosyncrasy Dissection
and Assorting of Brain MR Images Instigating Kernel Corroborated
Support Vector Machine,” Archive Des Science
[27] Vijayakumar B., and Ashish Chaturvedi, “Abnormality segmentation
and classification of brain MR images using Kernel based Support
vector machine,” Archive Des Science, Volume 66, Issue 4.
[28] B. Vijayakumar, Ashish Chaturvedi and K. Muthu Kumar, “Brain
Tumor in Three Dimenesional Magnetic Resonance Images and
Concavity Analysis”, International Journal of Computer Application,
Issue 3, Volume 1 (February 2013).
[29] B. Vijayakumar, Ashish Chaturvedi and K. Muthu Kumar, “Effective
Classification of Anaplastic Neoplasm in Huddling Stain Image by
Fuzzy Clustering Method”, International Journal of Scientific
Research, Issue 3 volume 2, March-April 2013.
ISSN: 2231-5381
http://www.ijettjournal.org
Page 92