Automatic brain stroke detection using histogram
based classification methods
Srikanth Busa#1,
Dr. E. Sreenivasa Reddy*2
#
*
Associate Professor, CSE Department,
PSCMR College of Engineering & Technology,
Vijayawada, A.P, INDIA.
Professor & Dean, CSE Department,
A.N.U. College of Engineering & Technology
Acharya Nagarjuna University., GUNTUR, A.P, INDIA.
1srikanth.busa@gmail.com
2edara_67@yahoo.com
Abstract— A computer aided stroke detection techniques are
brain. Tumors can do harm to appropriate brain tissues by
useful for diagnosing brain tumors or strokes. Human brain
exhibiting congestion, force on brain parts and then multiplies
stroke is the rapid loss of brain functions due to hemorrhage.
pressure in the skull. For the last 10 years we have been
Image classification and segmentation are used to explore
observing an active increase in research works in the area of
different types of strokes. Since stroke detection using
cerebral cancer diagnosis.
classification or segmentation is automated approaches, which
A large number of research centers[1] are concentrated on this
will minimize the detection time. Histogram or Centroid based
segmentation methods like K-Means, Mean-shift segmentation
fail to detect optimal regions from high resolution images. In the
high resolution images, segmentation main aim is to divide the
issue, since the truth is cerebral cancer is spreading all over
the world population. For instance, in the U.S almost three
thousand children are suffering with brain strokes. Nearly 50%
image into a set of non-overlapping regions based on stroke
will end their lives within the span of 5 years, processing the
features. Traditional approaches have been investigated to get an
highest death rate among children[2].It is also related with
optimal solution for the stroke detection. Automated brain stroke
neurological disabilities, psychological problems, retardation
detection approaches are difficult due to variations in size, type,
and increased risk of death. Despite from all these issues,
shape and location of strokes. Histogram based stroke detection
deaths due to cerebral cancer were increased among the world
can be used to find the stroke in the left or right symmetrical
population. Among all the countries, Africans are more
regions. Experimental results show that histogram based
detection has advantages as well as limitations compare to
segmentation based approaches.
victims of this disease. It was observed that in Tunisia the
cancer death rate was increased to 14.7% among the elder
people.
Thereafter,
the
next
leading
disease
was
cardiovascular diseases[3]. Due to its undesirable effects on
Keywords—,
Hemorrhage,
Image
Segmentation,
Tumors,
Strokes, Wavelet Transformation
victims, stroke diseases, establishing large responsibility on
the nation's economy and society [3].
I. INTRODUCTION
Most of the present traditional diagnostic approaches are
An abnormal growth of tissues within the skull called brain
depending on human experience in explaining the MRI or CT
stroke. Actually, it develops from blood vessels, nerves and
scan for judgement; this will increases the probability of false
cells of the brain which evolved from the brain. Generally,
discovery and detection of the brain stroke. On the other side,
two types of strokes exist, one is benign cancerous strokes and
applying
the other is injurious strokes. Injurious strokes are slow
identification and quick detection of the stroke [7]. One of the
growing strokes and will not spread to other tissues of the
most efficient approaches to get data from the critical medical
digital
image
processing
gives
the
precise
images which has wide usage in the medical field is the
segmentation procedure. The important objective of the image
segmentation is to divide an image into exhaustive and
mutually exclusive regions such that each interested region is
Medical Image
continuous and pixels within the area are same with regard to
the predefined condition.
Widely used conditions include values of texture, range,
Preprocessing
intensity, color, surface curvatures and surface normal.
Traditionally, a brain tumor or stroke can be detected by using
color based segmentation such as k-means, agglomerative and
histogram approaches with edge detection algorithms. Digital
Segmentation
Algorithm
image segmentation technique plays a significant role in all
the medical applications, and the key usage of this
segmentation is to partition an image into areas and objects
Number of
Segments
which correspond to regions or real world objects and the
extend of sub-division based on the specific application
Fig.1 Traditional Medical Segmentation Process
requirements.
In all the medical image segmentation, objects related to real
This paper provides the study of segmentation approaches on
world objects could not be gained without proper inputs from
medical images. Traditional study has limitations on
the user or specific knowledge of the problem domain.
classification and over segmentation.
Medical image feature selection is an important requirement
II. LITERATURE SURVEY
for most of the segmentation techniques. Depending on these
extracted features, the segmentation approaches are classified
into 3 categories, namely, edge-based, region based and
thresholding based segmentations.
In the Fig.1, medical image is given as input to pre-processing
approach. Here, the pre-processing algorithms are used to
remove noise or to enhance the brightness of the high
In [9-10], color texture analysis to extract spectral correlation
features is proposed. Both approaches doesn’t handle texture
boundary extraction due to noise in the image. A more
improved approach is proposed in the Texem model [11],
which consists of a conditional dependency between neighbor
regions and it is totally based on Gaussian mixture model.
resolution spectral images. In the segmentation step traditional
techniques like K-means, Graph based segmentation. FuzzyMeans, etc. are used. Finally, the number of segmented
regions is identified.
Digital image thresholding is one the most popular method
due to its simple implementation and intuitive features[11].
Consequently, the threshold
measures for
multimodal
histograms must be minima among the two maxima. A few
techniques enlighten the histogram peaks in image feature
extraction stage so as to provide the threshold detection.The
main drawback of this method is to segregate the object from
background if the object and background regions are identical
pixel distribution.Edge based segmentation works well against
irregularities in image attributes such as texture,gray level,
color etc. These irregularities are known as edges and are
gray level, texture, etc.) lies within a certain range belongs to
noticed using edge detection operations, some of the generally
the identical class and good segmentation results include only
used operations are prewitt, laplace , sobel etc. Segmentation
two opposite components can be achieved.
using edge-based method could be used as incomplete due to
Jaskirat Kaur, Renu Vig.’s and Sunil Agrawal[9] paper
the occurrence of stray, broken or noisy edges. Enhanced
implemented edge detection and thresholding as one of the
image
main aspects of image segmentation comes prior to image
processing
is
essential
to
extract
the
edges
corresponding to rational objects. Several edge based methods
recognition system and
feature extraction for analyzing
have benn proposed in the literature, but the frequently
tumors or strokes. In this approach, edge detection and
accepted segmentation systems are edge based thresholding,
thresholding techniques are implemented on different medical
which is used to clear noisy edges in bright conditions. Edge
images, geo images to quantify the stability of error rates. Y.
image thresholding directs to stray edges in the presence of
Zhang,V. Dey, M. Zhong proposed threholding algorithm to
noise where the actual edges are frequently missing[11]. Stray
detect edges and removing noisy regions using histogram
edge problems can be cleared if the edge properties are
peaks.
decided with respect to the mutual neighbor, while the
comparison of these methods are executed based on the
presence of edge based on the strength of edges in the near
assessment criteria and classification method to analyze
neighborhood. Region based segmentation approach which
performance metrics. Experimental results show that benefits
depends on the homogeneity measure to divide and merge
and limitation of new methods, and
regions in an image so as to broaden semantic or useful
capabilities regarding the evaluation procedure. Chenyang Xu,
division in the processed image.
Dzung L. Pham, Jerry L. Prince implemented the thresholding
Zhang implemented the image analysis and
methods on scalar images using
facilitate additional
binary partitioning and
Conventional Stroke Detection Methods:
image intensities. Segmentation is the process of grouping all
Self Organizing Map (SOM), as part of competitive learning
related intensity pixels more than the user specified threshold
neural network technique has been used to develop the vector
into one class, and all other pixels into second class is called
quantization process.The role of SOM for vector quantization
multi thresholding.
process is mainly due to the pixel similarity between the
Magnetic
region learning applied in the self organizing map. Neural
tomography (CT) are the two types of stroke image types that
units in the competitive layer need to be nearly equal to the
are regularly used for brain/stroke imaging. Computed
number of regions specified in the segmented image. This is
tomography imaging is preferred over Magnetic resonance
the main disadvantage of traditional SOM for image
imaging due to lower cost , wider availability and
segmentation. The HSOM straightly addresses the drawbacks
sensitiveness to prior stoke. In most situations, CT represents
of the traditional SOM. HSOM is the mixture of self
required information to make robust decisions during severe
organizing and topographic mapping technique. HSOM
cases [2]. The stroke contrast begins with the poor in the
combines the idea of data abstraction and independent of
primary stages and improves over period as shown in Fig. 2.
image features.
This is due to the density of the infarct area changes with the
Yau Elmagarmid , Jianping Fan, and Aref’s [2] paper
time until it spread the density to the cerebral spinal fluid. Fig.
implements
2 shows the early and late stage of ischemic stroke[1].
an automatic image segmentation technique
using thresholding approach. This is based on the preassumption that neighbor pixels whose value (color, value,
resonance
imaging
(MRI)
and
Computed
In the initial stage, each encephalic brain slice into three
classes Class 1, Class 2 and Class 3 as discussed earlier,
depend on their histogram intensity values. And then left and
right hemispheres are extracted for histogram computation.
Correlation similarity is computed to each left and right
hemisphere and then compared to each other. And in the
second stage, a classification mechanism is used to
Fig 2: Ischemic Stroke
differentiate acute and normal infarct cases.
Problems in Traditional Brain Strokes Algorithms:
Input : CT Brain Slice

Conventional
Segmentation
algorithms
fail
to
differentiate the stroke region with the corner edges.

Chronic Infarct
A-N Infarct
Chronic stroke detection depends on the histogram
and user specified threshold. Need to specify a
Hemorrhage
dynamic or global and local threshold.

Fails to identify the symmetrical hemispheres due to
noisy pixels in the central region.
Acute Infarct (A)
Normal Infarct (N)

Fails to detect new types of strokes in the test data.

Detects only strokes in the left or right hemispheres
regions.
Fig. 3. Brain Stroke Classification flowchart.
III. TRADITIONAL EXPERIMENTAL RESULTS
Chronic and Hemorrhage infarct can be detected using
Histogram-based comparison, whereas normal and acute cases
are not so easily identified from their feature histograms. In
these two cases, wavelet transformation based texture
information is used for stroke detection. Fig 3: shows the two
phase classification presented in [1]. In the first phase, an
input brain slice is categorized into three classes: Class 1:
chronic infarct, Class 2 Hemorrhage and Class 3 acute or
Normal infarct. And in the second phase, Class 3 is divided
into two sub-classes: Class 31 Normal infarct and Class 32
normal infarct.
The traditional algorithm[1] has three major steps.
1) In the first step, an input brain slice is denoised and
enhanced.
2) In the second step, the brain symmetry is identified.
3) And finally, the strokes slices are recognized.
Fig 4: Stroke Type1 Image
Fig 5: Left and Right hemisphere plotting.
Fig 7: Problem in Stroke Detection due to noisy edges
IV CONCLUSION
In this paper, we have summarized the various stroke
detection approaches and its limitations on medical image
database. The study also reflects the various histogram models
used for threshold based segmentation. These segmentation
algorithms discussed are essential for identifying stroke edges
and interesting regions. Automated brain stroke detection
approaches are difficult due to variations in size, type, shape
Fig 6: Stroke type 2
and location of strokes. Histogram based stroke detection can
be used to find the stroke in the left or right symmetrical
regions.
Experimental
results
show
that
conventional
techniques have both advantages and limitations for detecting
strokes in real time CT images.
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Fig 7: Left and Right hemisphere plotting
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