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Computer Aided Diagnosis System For Brain Disease Analysis
110 Int J Res Med. 2016; 5(3); 110-121 e ISSN:2320-2742 p ISSN: 2320-
2734
Computer Aided Diagnosis System For Brain Disease Analysis
Ranjita Chowdhury1, Sudipta Roy
2, Samir Kumar Bandyopadhyay
3*
1Professor, Department. of Computer Science and Engineering St. Thomas‟ College of Engineering & Technology, Kolkata ,
West Bengal, 2,3Department of Computer Science & Engineering University College of Technology, University of Kolkata,
JD-2, Sector-III, Salt Lake, Kolkata 700098, India
INTRODUCTION
Medical imaging is a routine and essential
part of medicine where computerized
applications are used to assist clinicians
and radiologists to carry out daily
activities within healthcare. A number of
applications include computer aided
pathology diagnosis, computer aided
image segmentation, planning and guiding
treatment, and monitoring disease
progression based on the information
extracted from medical images. The major
advantage of this field is that health
problems can be observed directly rather
than derived from the symptoms. Health
problems can, be broken bones, brain
abnormalities, breast, and prostate cancer.
In this synopsis attention is put on
computer aided abnormal brain lesions
diagnosis from magnetic resonance
imaging (MRI). A lesion is an abnormal
lesion of the brain tissues which suppress
and occupy the normal lesions area.
Various factors that lead to abnormal brain
lesion development include brain injuries,
multiple sclerosis, hemorrhage, stroke,
vascular disorders and brain tumors. Brain
lesions are often a threat to life hence their
diagnosis and treatment is of great
importance to patients . Nowadays,
different imaging modalities are used to
acquire medical images for visualization of
internal human body structures such as
tissues of the brain and neck. The most
common imaging technologies are
computed tomography (CT) and MRI. The
advantage that MRI has over CT is that it
is harmless, since it does not use ionization
radiation, and produces high quality
images with soft tissue contrast that is
much better than that with CT1. Moreover,
MRI can distinguish tissues that have
similar intensities and are hard to
distinguish using CT scans. Proposed plan
for automated brain anomaly segmentation
are developed and applied to a large
dataset of brain PD, T1- and T2-weighted
MR images.
Problem Statement: Hand labeling of
brain pathologies in medical images is
often regarded as the gold-standard
technique to segment brain abnormalities.
This method is currently used in many
laboratories by a radiologist to monitor the
response of the brain tumors and other
abnormality before and after the treatment.
However, this approach becomes tedious
in the presence of small sized brain lesions
and time consuming due to the large
amount of data to be analyzed or the
presence of multiple tumors having
different sizes. Moreover, the results are
usually operator-dependent. So it is
necessary to develop an automated
computer aided brain pathology diagnostic
application. It can save radiologists time in
setting up a suitable treatment for a patient
diagnosed with brain tumors. Numerous
automated methods for segmenting brain
pathologies have been developed. These
methods vary depending on the
characteristics of the abnormality to be
ORIGINAL ARTICLE
ABSTRACT
BACKGROUND: Computer-aided diagnosis (CAD) systems have been the focus of several research endeavors and
it based on the idea of processing and analyzing images of different hemorrhage of the brain for a quick and accurate
diagnosis. This paper proposed for automated brain anomaly segmentation are developed and applied to a large
dataset of brain PD, T1- and T2-weighted MR images.
Key Words: Brain MRI Scans, CAD Systems, Image Processing, Image Segmentation
Computer Aided Diagnosis System For Brain Disease Analysis
111 Int J Res Med. 2016; 5(3); 110-121 e ISSN:2320-2742 p ISSN: 2320-
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segmented and the type of image modality
used2. The lesion attributes is a
challenging task for automated
segmentation since it includes the variety
of shapes and sizes the lesions may
possess. Additionally they have a
likelihood of appearing at any location and
with different intensity distributions.
Because of these factors, there is no
general brain lesions segmentation method
which can be adopted widely in every
application.
OBJECTIVES
Implement an efficient computer aided
diagnosis (CAD) technique for brain
pathologies are quite useful. The other
objective is to evaluate the accuracy of the
implemented methods against the ground
truth. The methods solve to reduce the
false detection, spurious lesion generation,
under/over segmentation problems. This
will determine how well the methods
perform under varying condition. The
remaining parts of this document are
organized as follows. Section 2 presents
the few literature survey of the different
computer aided diagnostic methods used to
detect and segment brain lesions. Section 3
represents about the contribution of our
work and finally we conclude our paper in
section 4.
Literature Review: Brief background
knowledge about the detection and
segmentation of brain abnormality is very
useful to implement computerized
prediction and analysis abnormality in MR
images with high accuracy and low error
rate. Cherifi et al.4 implemented a
classification method based on expectation
maximization segmentation. Their method
is automatic and works for both tissue
recognition and tumor extraction. Jafari et
al.5 present a neural network-based method
for automatic classification of brain MR
images. Their method classifies tissues
into three categories: normal, tumor
benign and malignant. They use the
discrete wavelet transform to obtain
features related to each MRI and applied
principal component analysis to reduce
feature dimensions to obtain more
meaningful features. After the essential
features have been extracted, a supervised
feed-forward back-propagation neural
network technique is used to classify the
subjects. Supervised brain tumor detection
methods rely on the use of manually
annotated training data. These methods
develop the model from the training data
set and use the same model to recognize
new test data at a later stage. The major
disadvantage4,5
of these methods is that
they are labor intensive and time
consuming. Also they are likely to fail to
perform well if there is an overlap of
intensity distributions between healthy and
abnormal brain tissues. Pedoia et al.6
design a fully automatic technique to
detect brain tumors using symmetry
analysis and graph-cut clustering methods.
Their approach reflects the right
hemisphere and computes voxel by voxel
differences from the left hemisphere and
the mirrored right hemisphere to derive a
volume that highlights the regions with
greater intensity difference with respect to
the background as asymmetric
components. Graph-cut is then used to
extract this area and the resulting region.
The normalized histograms of the left and
right hemisphere are computed and
histogram analysis is performed to
recognize the ill hemisphere. The
limitation of their method is that it only
recognizes hyperintense tumors, sensitivity
to noise and inhomogeneity of tumors
Due to variety of brain tumor types
and their manifestation in MR images,
most state of- the-art methods focus on
most common tumor types, i.e.
glioblastoma, or they a require specific
training database to deal with a specific
tumor type. Only few researchers, like
Islam et al.7, tried to train a developed
algorithm on one tumor type and test it on
another. However, the results were not
satisfying. In common clinical routines,
the evaluation the acquired images is
currently performed manually based on
quantitative criteria or measures such as
the largest visible diameter in axial slice.
Therefore, highly accurate methods being
able to automatically analyze scans of
brain tumor would have an enormous
potential for diagnosis and therapy
planning. However, it was shown by
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Menze et al.8 that even manual annotation
performed by expert raters showed
significant variations in areas where
intensity gradients between tumorous
structure and surrounding tissue are
smooth or obscured by bias field artefacts
or partial volume effect. Moreover, brain
tumor lesions are only defined by relative
intensity changes to healthy tissues, and
their shape, size and location are
individual for each patient, which makes
the use of common pattern recognition
algorithms impossible. Segment only the
solid section of the tumor, edema and
necrosis were not considered. Saha et al.9
located tumors in 2D MR images in axial
plane using the fast detection of
asymmetry by Bhattacharyya coefficient.
The output of the algorithm was the
bounding box around the tumor. Davuluri
et al.10
presents a rule-based hemorrhage
segmentation technique that utilizes pelvic
anatomical information to segment
hemorrhage accurately. The results show
that the proposed method is able to
segment hemorrhage very well, and the
results are promising. The results show
that the proposed method is capable of
segmenting hemorrhage well. Automated
hemorrhage segmentation, once verified
with more data, will be an important
component of computer assisted decision
making system. But, quantitative
measurement of hemorrhage such as
determining hemorrhage volume,
identifying the location of hemorrhage
does not carry out with respect to the bone,
and so forth on the basis of larger data set.
Mahmood et al. 11
presents an evaluation of
several methods using both synthetic MRI
data and real data from four healthy
subjects. The methods were evaluated in
terms of: i) tissue classification accuracy
over all tissues with respect to ground
truth, ii) the accuracy of the simulated
electromagnetic wave propagation through
the head, and iii) the accuracy of the image
reconstruction of the hemorrhage. The
segmentation accuracy was measured in
terms of the degree of overlap (Dice score)
with the ground truth. The results show
that the automatic segmentation method
hierarchical segmentation approach-
Bayesian adaptive mean shift12
has better
performance with higher segmentation
accuracy, lower signal deviation and lower
relative error compared with the other
methods. The results also indicate that
accurate segmentation of tissues leads to
accurate reconstruction of intracerebral
hemorrhage in the subject‟s brain. Prakash
et al.13
proposed a modified distance
regularized level set evolution for
hemorrhage segmentation. Method
included pre-processing as filtering and
skull removal, segmentation with modified
parameters for faster convergence and
higher accuracy and post-processing which
reduce the false positives and false
negatives. The method generates
quantitative information, which is useful
for specific decision making and reduces
the time needed for the clinicians to
localize and segment the hemorrhagic
regions. Ballin et al.14
introduce a multi-
scale approach that combines
segmentation with classification to detect
abnormal brain structures in medical
imagery, and demonstrate its utility in
automatically detecting multiple sclerosis
(MS) lesions in MRI. It produces a rich
set of features describing the segments in
terms of intensity, shape, location,
neighborhood relations, and anatomical
context. These features are then fed into a
decision forest classifier, trained with data
labeled by experts, enabling the detection
of lesions at all scales. Unlike common
approaches that use pixel-by-pixel
analysis, it utilizes regional properties that
are often important for characterizing
abnormal brain structures. Biediger et
al.15
take different approaches to the
problem of lesion segmentation and
include a number of steps, including pre-
and post-processing. It introduces a two-
step approach to improve the results of an
existing automated segmentation method.
It compares all the results to the expert
segmentations for each patient. Major
disadvantage of this method is it suffers
from spurious lesions generation. Jaina et
al.16
, propose MSmetrix, an accurate and
reliable automatic method for lesion
segmentation based on MRI, independent
of scanner or acquisition protocol and
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without requiring any training data. The
actual lesion segmentation is performed
based on prior knowledge about the
location and the appearance of lesions.
Finally, the accuracy and reproducibility
of MSmetrix compare favorably with other
publicly available MS lesion segmentation
algorithms, applied on the same data using
default parameter settings. Over
segmentation is the major problem of this
method. Over or under segmentation of
normal brain tissue and non brain part are
performed by the existing segmentation
methodology. Despite the enormous
amount of work that has been done there is
no widely accepted method to do this task.
Based on these facts, finding an automated
and accurate brain lesion detection and
segmentation method is useful and gives
researchers an opportunity to come up
with new ideas in trying to solve the
different problems.
CONTRIBUTIONS
Data set selection: For experimental
analysis images available in the
public domain are utilized that are
utilized by several research
organizations those are conducting
similar research. We have also used a
Harvard medical dataset (available:
January 2014,
http://www.med.harvard.edu/aanlib/h
ome.html) with whole brain atlas and
different type of brain diseases.
BrainWeb: Simulated Brain Database
with normal structure and ground
truth (
http://brainweb.bic.mni.mcgill.ca/brai
nweb/). We also used EASI MRI
Database for different brain
abnormality MR images
(http://www.easidemographics.com/cgi
-bin/dbmri.asp).
Pre-processing
Artefacts Removal: Many different
artefacts can occur during MRI, some
affecting the diagnostic quality, while
others may be confused with pathology.
Thus to detect any abnormalities in
brain like tumor, edema must remove
artefact otherwise it may treated as an
abnormalities in automated system or
may hampered the intelligence system.
In the first stage, threshold value is
calculated over a image to binarized a
image. A statistical method i.e. standard
deviation is used to calculate the
threshold value. In this processing
statistical descriptions separate
foreground images and background
images. Then maximum and the second
highest connected component area are
found out. The ratio of the maximum
area to that of second maximum area
are calculated if the ratio is high
(signifies that the skull are brain are
together as one component as explained
above) and if ratio is low (signifies the
skull and brain are two different
component as explained above). Then
on basis of the ratio if ratio is high then
only the component with highest area is
kept and all others are removed
otherwise if ratio is low the component
with the highest and second highest
area are kept and all others are
removed. A convex hull is calculated
for the one pixel in the image and all
regions within the convexhull are set to
one. Now the above obtained image
matrix is multiplied to the original
image matrix to obtain an image
consisting of only brain and skull and
without any artefact. Results of
proposed method have been shown in
Figure1.
Figure 1: (a) input MRI of brain image
with artefact and (b) is the
corresponding output MRI
(a) (b)
Figure 1(b) is the brain image without any
artefact. It is very helpful for the further
process like skull removal, tissue detection
and abnormality detection. Our method
does not loss any information within brain
region.
Skull Elimination: Skull removal is an
essential task for accurate detection of
brain abnormality from MRI of brain
otherwise spurious lesion may generated
Thus elimination of this a problematic
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area of brain improve the diagnosis
quality of brain by intelligence system.
Here artefact and skull elimination
processes by the automated system has
been proposed. At first binarize the
image using the statistical standard
deviation method. Then complement of
binarized image and two dimensional
wavelet decompositions is done using
„db1‟ wavelet up to level two. Re-
composition of the image is done using
the approximate coefficient of previous
step. Interpolation method is used to
resize the image of the previous step to
the original size. Then labeling of the
image is done using union find method.
After that maximum area of all the
connected components is found out this
represents the brain. All other
components except the maximum
component are removed from the image.
Convex hull is computed for these one
pixel and the entire pixels inside the
convexhull are set to 1 and outside it are
set to zero. The image of the previous
step is multiplied to original image pixel
wise and thus segmented brain is
obtained. Result of skull elimination
method has been shown below in
Figure2.
Figure 2: (b) is output MRI image
without border and (b) is the input MRI
image
(a) ( b)
MRI of brain without skull region is
shown in Figure 2 (b). It is very clear in
Figure 2(b) that only skull portion is
removed, main brain region remain same.
Magnetic Resonance of brain image
Binarization: Many segmentation needs
binarization as pre-processing as
intermediate state. MRI of brain image
binarization is very difficult due to many
pixels of brain part cannot be correctly
binarized due to extensive black
background or large variation in contrast
between background and foreground of
MRI. We have proposed a binarization
that uses mean, variance, standard
deviation and entropy to determine a
threshold value followed by a non gamut
enhancement which can overcome the
binarization problem of brain
component. The binarization technique
is extensively tested with a variety of
MRI and generates good binarization
with improved accuracy and reduced
error. Proposed method is divided into
two phases; in the first phase we enhance
our brain part and in the second part we
calculate threshold value. In first phase
foreground image contrast enhancement
techniques involve scaling and shifting
operations; the net result of these
operations on an image is that all its
pixel values above a certain reference
value, with respect to that particular
image, are pushed to a higher value
while all the pixels with level below that
point are pushed to lower gray values. In
second phase we calculate final
threshold value for the binarization using
entropy and standard deviation from the
gray MR image. Thus the proposed
binarization method is a concatenated
application of gamut less enhancement,
mean, variance, standard deviation and
entropy calculation and our new
binarization method also act as a
preprocessing of MR image of brain
image. The result of our proposed
method has been shown below in Figure
Figure 3. Input brain image (a), output
binarized image by proposed method
(b)
(a) (b)
Our method able to binarized only brain
region and it does not consider the black
back ground. In most of the binarization
technique used to binarized whole image.
But only brain region is the goal of
interest, our method binarized the human
head region and it is shown in From Figure
3(b).
Tissue detection & segmentations
Normal major tissues segmentation
Segmentation of brain tissues on MRI
images generally decides the information
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and prior knowledge of the brain. In this
section, an iterative implement of level set
methodology has been proposed for the
precise segmentation of normal and
abnormal tissues in magnetic resonance
imaging (MRI) brain images. In this
segmentation, the normal tissues such as
WM (White Matter), GM (Gray Matter)
and CSF (Cerebrospinal Fluid) with other
part of human head such as marrow, and
Muscles skin are segmented and abnormal
tissues such as hemorrhage; edema and
tumor can be segmented if any. The
segmentation done by using iterative three
region level set method based on the
concept of sharp peak. The iterative
segmented component is generating a
hierarchical structure to make correct
segmentation. We have use three phase
level set as a basic segmentation and
without using mask concept. As accuracy
is an important factor in medical imaging,
thus to improve the accuracy we using
iteration on level set as key concept.
Iteration used to divides this complex
segmentation to make segmentation easier
and accurate. Calculating the sharp peak
from histogram representation of the
images and depending on this sharp peak
we repeat our task. Once we have
performed the three region level set
segmentation with three membership
functions we clearly find out three regions.
In the proposed approach, we model the
intensity distribution in the image
partitions of level set using a Gaussian
mixture model to form a close
approximation to the actual intensity
distribution in the image. The model is
forced to inflate on smooth areas and to
stop at high-gradient locations as the speed
decreases towards zero. We have chosen
peak value three because of three region
segmentation. The block diagram of the
concept of iteration of proposed method
has been shown below in Figure4. Main
concept of segmentation is given below in
Figure 4.
Figure 4: Block diagram of Iterative
segmentation method
The procedure of peak calculation is
prepared by choosing previous three and
next three neighbor positions for each gray
value k in a circular manner into (k-3)(k-
2), (k-1), (k+1), (k+2) and (k+3)
respectively. We select sharp peak only
on pixel which is greater than previous
three and less than next three pixel
intensity. After applying above method
when segmentation method stopped, we
use maxima area MA to extract maximum
area between two regions of brain, and
maximum area always appear as left child.
Brain tissues and different fragments of
skull segmentation of normal brain are
possible by up-to level 3 decompositions
and re-compositions. In other word in level
2 all decomposed fragments have sharp
peaks less than or equals to 3. If any
abnormality presence in the brain then it
does not follow same structure shown
below and abnormality need to decompose
it greater than level 3. The process of
proposed method to accurate segmentation
has been shown below in Figure5 as
hierarchical diagram.
Figure 5. Hierarchical diagram of
tissues detection methodology for
normal brain structure. If any
abnormality is present it is detected in
level 2 and numbers of sharp peaks of a
node in level 2 will greater than three
From the above figure5 it is clear that
initially normal brain image (BI) segment
into two regions BI1 (contains WM, GM,
marrow, and muscles skin) and BI2
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(contains GM, CSF, and muscles skin).
Inputted brain image is treated as level 0
and BI1 and BI2 treated as level 1.
Number of sharp peak greater than 3 at
level 1 so we repeat the segmentation and
the segmented region of level 1 produce
the level 2. Segmented part of level 2 does
not have any sharp peak greater than 3 for
normal brain. For the entire segmented
region we place maximum area as left
child. If any segmented region in level 2
has greater than 3 number of sharp peaks
then we consider some abnormality are
present in the brain. Region BI12 and BI21
both contains GM and muscles skin, so we
add this segmented region to improve the
accuracy. Region BI11 contains WM and
marrow, and BI22 contains CSF and fats.
Finally we segment WM, marrow, GM,
muscles skin, CSF, and fats by using max
area from left to right for each parent node.
If any abnormality present we can detect it
in level 2, we also use abnormality
position detection method which is
describe in next section and rest of the
region are treated as normal segmented
tissues.
Figure 6: a) Inputted MRI, b)
segmented marrow, c) segmented white
matter, d) segmented CSF, e) segmented
gray matter, f) segmented muscles skin
(a) (b)
(c) (d)
(e) (f) The above Figure 6 is the results of
segmented regions of normal tissues. It
also removes over and under segmentation
problem of other comparable method.
Proposed method gives very good results
for segmentation of different regions from
the visualization point of reference. From
the above figure 6 it is easy to find out the
area of each segmented region. Once
quantify WM, GM, and CSF, and scan a
period of time then we can predict about
Alzheimer disease. On the other hand,
brain segmentation is a preliminary step
for the other procedures such as brain
registration, warping and pixel based
morphometry.
Corpus Callosum segmentation Corpus
Callosum (CC) is an important part of
the brain which works as major neural
pathway that connects homologous
cortical areas of the two cerebral
hemispheres. The size of CC is affected
by age, sex, neurodegenerative diseases
and various lateralized behavior in
people. Here T1 weighted MRI of brain,
usually the sagittal sections is taken
which is then followed by the automated
segmentation of the MRI slice. The
proposed method includes the following
phases: (1) Input of T1 weighted image
and refining of the image to reduce noise
(2) Segmentation of CC using area
selection and binary conversion of image
(3) Coloring of detected part in original
image. Segmentation is performed by
using binarization algorithms and
maximum area selection from sagittal
image. The work flow of our proposed
methodology can be represented by
Figure 7.
Figure 7: Workflow of the proposed
methodology
Result of CC segmentation has been
Figure 8: (a) Original image, (b)
segmented region, (c) corpus callosum
in green color
(a) (b)
(c)
Input
MRI
slice
dataset
Image
correction
and filtering
Gray-scale
and
normalization of images
Segmen
tation of CC
Detection of
mid-point and
end points for bending angle
Plot the
bending
angle of all the slices
Collect all the segmented CC to produce a 3D projection
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The segmentation method gives us the
most suitable result as per T1 weighted
MRI images are involved. In this result we
highlight the detected portion of CC and
keeping rest of the image untouched.
Results have been tested on collected
datasets each consisting sagittal plane MRI
slices with different variations of input.
Abnormality detection &
segmentation
Detection of tumor, hemorrhage and
stroke lesions
Accurate identification of correct
abnormalities is a critical step in planning
appropriate therapy. The correct
characterization of underlying pathology,
such as neoplasia, vascular malformation,
or infarction, is equally important for
conclusive diagnosis. The implemented
algorithm includes several stages such as
artefact and skull elimination constituting
preprocessing, image segmentation, and
abnormality localization. After artefact and
skull removal power law transformation
has been performed. Transform values of
γ>1 have accurately the opposite effect as
those generated with principle values of γ
<1 and to the identity transformation when
c= γ =1. Gamma correction is significant
for displaying an image appropriately on a
computer screen and particular care must
be taken to reproduce colors accurately.
By using this gamma transformation the
abnormal portion can be more prominently
projected. The total intensity, by sum of
the average and standard deviation of the
gamma transformed image is finally
selected. On the basis of the final intensity
value we find the abnormal portion which
in the form of binary output. The first
derivative denotes zero in areas of constant
gray-level values while non-zero at the
onset of a gray-level step or ramp; and
must be nonzero along ramps. As
horizontal and vertical contour detection
does not produce continuous line so, we
combined it to produce continuous line.
The results of abnormal segmentation
method have been shown below in figure
9.
Figure 9: a, f) input MRI of brain
image; b, g) abnormality visualized by
red region; c, h) contour of abnormality
region (Chronic subdural hematoma &
Cerebral hemorrhage)
a) b)
c) f)
g) h)
From figure 9 we can measure the clot
thickness, abnormal area, and localization
of lesion from MRI scan have been
successfully implemented by the proposed
system. This is very crucial for early,
reliable and accurate detection of
abnormality for providing early diagnosis
and treatment, prompt transfer of the
patient to a medical facility capable of
MRI scanning and neurological
intervention if necessary.
Multiple sclerosis lesions
identifications: Multiple sclerosis (MS)
is one of the major diseases and the
progressive MS lesion formation often
leads to cognitive decline and physical
disability. A quick and perfect method
for estimating the number and size of
MS lesions in the brain is a key
component in estimating the progress of
the disease and effectiveness of
treatments. In this section a method has
been described where adaptive
background generation and binarization
using global threshold are the key step
for MS lesions detection. Then
background image is subtracted from
binarized image to find out segmented
MS lesion. We have proposed a method
which will generate the background for
each image. Three phase level set is the
key idea to generate backgrounds for
each image. Contour detection
performed after artfact and skull removal
image. Then we add the level set image
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and the contour image to generate
adaptive background. To detect MS
lesion a global threshold selection
methodology has been done by the
combination of entropy and standard
deviation. Then subtract the binary
threshold generated image by
background image and finally get the
MS lesion. This approach better captures
the neighboring lesion properties and
produces encouraging results, with a
general improvement in the detection
rate of lesions.
Figure 10: a) input brain image, b)
segmented MS lesions, c) red portions
are affected in inputted brain image.
(a) (b)
(c)
Result of our method has been shown in
above figure 10. It is very clear that our
method find accurately identifying the
size, number of lesions and location of
lesion detections as a radiologist does.
The adaptability of the proposed method
creates a number of potential opportunities
for use in clinical practice for the detection
of MS lesions in MRI. Now look at Figure
10(b) and Figure 10(c), and you will see
three lesions of approximately the same
size and one lesion as different size. The 4
foci of inflammitory activity are clearly
not in synchrony. MS lesions may change
their position at the end of the year, this
lesion has almost disappeared, but another
has appeared just behind it, if some scan is
performed over different months. More
clear visions of different lesions with their
position have been shown below in Figure
11.
Figure 11: (a) Lesions in the top right
lobe, (b) Lesions in the near middle left
lobe, (c) Lesions in the bottom right
lobe, (d) Lesions in the bottom left lobe.
(a) (b)
(c) (d)
3-D representations from 2D slices and
Volume estimation: In the three-
dimensional (3D) construction of brain
tumour using several slices of MRI has
always been a keen interest for diagnosis
and for research purpose. In this section
we proposed an approach for 3D
construction and its volume calculation
from a series of two dimensional (2D)
MRI images. Each of the abnormality
detected MRI image are successively
pushed into a stack to construct a 3-
dimensional cube inside which it contains
the 3-dimentional constructed brain
abnormality. The volume of abnormality is
calculated from area of each MRI slices
with their inter slice distance. CAD system
tool helps the neurosurgeon to take
decision during their surgical planning. To
be able to do this, one first needs to
validate a detection and segmentation
methodology which has been done
previously.
Figure 12: Overall steps of 3D
visualization of brain abnormality
NO
YES
The flow chart for entire procedure of this
work is shown in Figure 12. This flow
After all images are inserted combine them to produce a 3D matrix
View 3D figure
STOP
Don‟t push into stack and
process next image. Push into image
stack and continue
process next
images.
START
Number of input MRI
images
Inputted images are in same dimension
Artefact removal
Detect brain abnormality
Abnormality
detected in
MRI?
Computer Aided Diagnosis System For Brain Disease Analysis
119 Int J Res Med. 2016; 5(3); 110-121 e ISSN:2320-2742 p ISSN: 2320-
2734
diagram represents the overall working
process of the proposed algorithm which
will give us a brief idea about the internal
working of the algorithm. As illustrated
from the Figure 12 we get the summarized
process of 3D construction of the brain
abnormality in the form of 3D figure
which is viewed using image processing
toolbox. The outputs of figure 13 by using
abnormality detection methodology are
shown in figure 14.
Figure 13: Inputted MRI of brain slices
to the algorithm (12 slides)
Figure 14: Detected tumour part in the
slices (as binary images)
A slice with no tumor cells is taken to be
an invalid slice and is rejected. The slices
containing tumor cells have white region
in them. These images serve as the input of
the 3D algorithm which will work on this
set of slices.
Figure 15. 3D construction output from
the binary images
In the figure 15 we see three outputs of the
3D construction algorithm. The 3D figure
generated can be rotated along any
direction and the wireframe box enclosing
the figure serves as the guidelines for the
output 3D figure while rotating it in any
direction along the axis.
Classification of Tumor: Detecting
correct type of brain tumor is a crucial task
for diagnosis and curing the tumor.
Identifying the correct type of brain tumor
can provide a fast and effective way to
plan the diagnosis of tumor. In the first
stage MRI image is taken as input and is
normalized. The second stage includes
extraction of feature vectors from the
image which results in reducing
redundancy of data and will serve as the
input to the classifier. The classifier takes
each row of feature extracted vector to
produce classified output. In this section,
we apply Adaptive Neuro Fuzzy Inference
System (ANFIS) to successfully classify
the input rows. The proposed methodology
is composed of multiple stages as
illustrated in figure 16. Initially we have
chosen tumor detection methodology from
MRI slices of brain. Then they are
normalized to an acceptable range before
being fed to feature extraction process. In
the classification step, the model is first
trained using training dataset obtained
from the image database which also
defines the class labels being used. After
the classification model is trained, it is
used to classify the testing dataset into
appropriate classes that will help us in
correct medical decision making and
diagnosis of brain tumor. After getting the
predicted output we compare them with
practical values to get the performance
measurement of the model being used. The
detailed implementation of the proposed
methodology can be given by the
following subsections.
Figure 16. Total workflow of the
proposed methodology
Each of the feature vector forms an input
rows to the classifier. These parameters
are taken to be parameters for feature
extraction they are Mean, Variance,
Skewness, Kurtosis, Entropy, Energy,
Correlation, Inertia (Contrast), Absolute
value, and Inverse Difference. Now let us
consider the following two fuzzy rules for
this model.
),(, :1 111 yxfzthenBisyandAisxIfRule
),(, :2 222 yxfzthenBisyandAisxIfRule
Using these two rules we now build an
adaptive network that can properly reflect
Input MRI Image
Normalization
Feature extraction &
creation of testing
dataset
Creation of training
dataset from image
database
Classification
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120 Int J Res Med. 2016; 5(3); 110-121 e ISSN:2320-2742 p ISSN: 2320-
2734
these rules when a mapping is done from
input to output product space.
Figure 17. ANFIS architecture
equivalent to fuzzy inference system
The adaptive network equivalent to this
fuzzy model illustrated in figure 2 where
we consider that each node in a particular
layer performs the same function. Each ith
node in the a particular layer l takes an
input from the previous layer and produces
an output ilO ,.
Figure 18. 20 input slices passed
through the normalization and feature
extraction processes.
After classification we found that slices I1-
I4 are Type 1 (Glioma); slicesI5-I8 are
Type 2 (Meningioma); slices I9-I12 are
Type 3 (Metastatic adenocarcinoma);
slices I13-I16 are Type 4 (Metastatic
bronchogenic carcinoma); slicesI17-I20
are Type 5 (Sarcoma) Automation of a
model for computing an estimate of the
type of tumor are verified by a radiologist,
and a simultaneous measure of the quality
of each phase is required to readily assess
the automated image classification and
segmentation algorithm performance. The
brain and tumor tissue identification
provides a better perceptive of the spatial
relationship; thereby lend assistance to the
adage of pre-operative treatment planning.
CONCLUSIONS
The basic objective of this research work
is to develop and integrate all the image
processing algorithms proposed on the
obtained dataset of slice images to attain
the deliverables. We plan to work with a
greater number of brain structures and
explore incorporating additional
information to guide our proposal. We
deal with two dimensional MR images in
order to detect the brain tumors and
features extraction for the applications
such as treatment and follow-up, surgery,
Individual modeling, etc. For
segmentation of the tumor we first
discussed the various type of
segmentation and detection procedure
very carefully. Analyzing the
performance of all steps gives us the
correctness of the procedure and
analyzing all steps we can say the type,
stage, dangerousness of the abnormality.
We evaluate the performance of different
section with different mathematical
metric which gives us very good results.
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