Neoplasm in Huddling Stain Image in FCM by K.Muthu Kumar, PSN college of engineering, melathediyoor
Transcript of Neoplasm in Huddling Stain Image in FCM by K.Muthu Kumar, PSN college of engineering, melathediyoor
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International Journal of Advanced Scientific and Technical Research Issue 3 volume 2, March-April 2013
Available online on http://www.rspublication.com/ijst/index.html ISSN 2249-9954
Page 1
Effective Classification of Anaplastic Neoplasm in Huddling
Stain Image by Fuzzy Clustering Method
1B.Vijayakumar and
2Ashish Chaturvedi and
3K. Muthu Kumar
1Research Scholar, Department of Computer Science and Engineering, CMJ University, Shillong,
Meghalya, India.
2Arni School of Computer Science and Application, Arni University, Indora (Kathgarh),
Himachal Pradesh, India.3PG Scholar, Department of Computer Science and Engineering, PSN College of Engineering,Tirunelveli,Tamil
Nadu,India.
_____________________________________________________________________________________
ABSTRACT
This paper presents a new attempt for classifying and analyzing the H&E Stain of distinctive types ofbrain tumor image and perceiving the region of brain tumor in the intracranial parts of skull in the human
body alsopredicting about the meticulous tumor type based on the features remains incognito. In braintumor images classification, it is indispensable to classify the numerous assortments of tumor tissue cells
for ascertaining more efficiently.The tumor prominently headway from either Glial cells andastrocytomas, oligodendrogliomas, ependymomas and the H&E Stain of various tumor tissue image
consists of a nugget of dissimilar objects incorporates necrosis tissue, normal tissue and H&E Stain of
microscopic images through Euclidean distance metric and Fuzzy K-Means clustering craft images thatsegment the H&E image by color separation for identification of quirky tumor. The classification
procedure has been applied to nine real data sets, epitomizing different orientation in the tumor tissue
region and augmentation through spatial constraints. Experimentation is carried out o the H&E Stain ofmicroscopic malignant brain neoplasm in MATLAB Environment.
Keywords: Classification, brain tumor,MRI image, Fuzzy,Clustering,microscopic image and H&E Stainimage_____________________________________________________________________________________I.INTRODUCTION
The perlustation of a brain tumor can be very daunting for patients and their families. Even though, the
Neurosurgeons perform a biopsy or removal of brain tumor, Neuro-oncologist prescribe chemotherapy
drugs for the treatment of brain tumors,radiation oncologists prescribe the radiation therapy for brain
tumors, the Neuroradiologists who helps to review the Scan based on X-ray, CT, PET, MRI, fMRI
image of brain tumor in extracted form and the Neuropathologists can only identify the tumor of types
cells based on the information provided by Computing through some Three dimensional volumetric
projection of extended atomic Microscopic images. Hence, it is very important to trajectile the brain
tumor extracted images and classify it through the trained set of images for automation computing. Theabnormal growth of cells arises from brain and around brain structures are generally considered as a
tumor or lesion. The causes of tumors in our body are still unknown and some of the possibilities include
exposing to high penetration ionizing radiation especially continuous usage of cell phones and heredity
concerns based on family history are surmountable towards brain tumor.The tumor,also called a mass that
grows inside the intracranial region of limited space of the brain and it expansion alters the brain cells
causes some symptoms like headache,vomiting, blurred vision ( visual abnormalties ) and speech
difficulties because of the damage in the left temporal lobe or motor cortex considered as focal symptoms
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International Journal of Advanced Scientific and Technical Research Issue 3 volume 2, March-April 2013
Available online on http://www.rspublication.com/ijst/index.html ISSN 2249-9954
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and leg cramps based on the grade of tumors.The brain tumors are classified into four grades based on the
intensity and it categories into Gliomas and non-Gliomas.The Gliomas involving in the supportive or glial
cells present in the White Matter of brain and there many types of glial tumors are
astrocytes,oligodendrocytes,Glioblastomas multiforme,brain stem gliomas, and the ependymal cells.The
Non-gliomas occur in other brain parts includes meninges (tissue coverings the brain) and nerve sheath
and the tumors arise from meninges cells are called meningiomas and tumor starts from nerve sheath are
called acoustic neuromas,vestibular schwannomas and neurilemmomas. The shape of the tumor differs
prominently and around 120 types of tumors are identified. The oligodendrocytes has fried egg shaped
cells and astrocytomas has star shaped cells [1].In this paper, we framed a new idea that combines image
processing computation towards microscopic image to identify the various types of tumors and
categorized.
Fig 1.1: Stastical data of brain tumor distribution by CBTRUS
Medical imaging helps to accurate measurement of the internal aspects of the structure of the tumor. The
Magnetic Resonance Imaging (MRI) is the most frequently used imaging technique in neuroscience and
neurosurgery for these applications. MRI creates a 3D image which perfectly visualizes anatomicstructures of the brain such as deep structures and tissues of the brain, as well as the pathologies.The
analysis of soft tissue boundaries of medical imaging involves a series of steps which includes extracting
the tumor image and analysis the microscopic tumor tissue and finally classifying the tissues based on the
Neural Network trainer for automated detection and computation of brain tumor types. The accurate
extraction of internal structures of the brain tumor is of great interest for the study and the treatment of
tumors. It aims at reducing the mortality and improving the surgical or radio therapeutic management of
tumors. In brain oncology it is also desirable to have a descriptive human brain model that can integrate
21.09
10.31
2.33.9
1.829.2
5.9
0.9
7.9
3.3
13.4
Gliblastoma
AstrocytomasEpendymomas
oligodendrogliomas
medulloblastoma
meningioma
pituitary tumor
craniopharyngioma
Nerve sheath
lymphoma
All Other
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tumor information extracted from MRI data such as its localization, its type, its shape, its anatomo-
functional positioning, as well as its influence on other brain structures. Existing methods lack significant
roles in the medical image classification characterization of abnormalities are still a challenging and
difficult task.
II. BRAIN ANATOMY
The Central Nervous System and peripheral nervous system are the two main nervous system present in
the brain structure and it consists of Gray Matter (GM) and White Matter (WM). The Gray Matter control
brain activity and cortex region cover the brain which is made of glial cells and the gray matter nuclei (colostrum ) are located deep within the white matter. The myelinated axons are considered as white
matter fibers that connect the cerebral cortex with other brain regions [3]. The cerebrospinal fluid (CSF )
consists of nutrition rich glucose,salts,enzymes and WBC's present between the lower part of brain andspinal cord.The meninges is present in the intra cranial of brain and act as protective layer.The cerebrum
parts of brain is divided into two hemisphere regions,the right and left cerebral hemisphere and consists of
four lobes including parietal,frontal,temporal and occipital lobe at the back of the brain.The cerebellum
located at the back of the brain and it consists of outer GM and internal wm.The brainstem connects to the
spinal cord consists of midbrain,pons and medulla oblongata.The diencehalon layer is the central structureof the brain and consists of thalamus,hypothalamus and pituitary gland and communicated throughventricles [4].
DENDRITE
MYELIN SHEATH
SOMA
AXON
NUCLEUS
Figure 1.2 Structure of Axon in human brain
In the vertebrate animal, the brain appears to be a most complex organ and contains many billions of
neurons depends upon the cerebral cortex,each connected through synapses or axon to another fewhundred of neurons and it communicates very quickly in irregular patterns that withstand fault tolerance.It carry trains of signal pulses and secrete hormones and it differentiate from Glial cells inside the human
body and it support metabolic support and structural support. The neurons are covered by a fattysubstance known as myelin sheath which contains rich in nerve fibers and hence the tumor cells has
tended to grow in foreign body and gain energy and similarly, Glial cells also involve in brain metabolism
through controlling the chemical fluids like ions and nutrients around the neurons and this is the mainreason tumor grows only in brain [4].
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III. BRAIN TUMOR CLASSIFICATION
A brain tumor is an intracranial mass produced by an uncontrolled growth of cells either normally foundin the brain such as neurons, lymphatic tissue, glial cells, blood vessels, pituitary and pineal gland, skull,or spread from cancers primarily located in other organs. Brain tumors are classified based on the type of
tissue involved, the location of the tumor, whether it is benign or malignant, and other considerations.Primary (true) brain tumors are the tumors that originated in the brain and are named for the cell types
from which they originated. They can be benign (non cancerous), meaning that they invade surrounding
tissues. They can also be malignant and invasive (spreading to neighboring area). Secondary or metastasisbrain tumors take their origin from tumor cells which spread to the brain from another location in thebody. Most often cancers that spread to the brain to cause secondary brain tumors originate in the breast,
and kidney or from melanomas in the skin [2].
Fig 1.3: Brain Tumor Classification by Worth Health Organization
An abnormal tissue that grows as mismanage cell division in our body system is considered as a tumor or
lesion.Brain tumors are named after the cell type from which they grow ( e.g: Gliblastoma, meningioma
or ependymomas ). They may be primary or secondary. Treatment options vary depending on the tumor
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type, size and location; whether the tumor has spread; and the age and medical health of the person.
Treatment options may be curative or focus on relieving symptoms. Of the more than 120 types of brain
tumors, many can be successfully treated. New therapies are improving the life span and quality of life for
many people [1], [8].
IV. MEDICAL IMAGE COMPUTING
The investigations to confirm the presence of a brain tumor tissue involves many modality techniques
based upon the intense usage of it. It includes CT Scan ( computed tomography ) identify the inner
aspects and structures of the inside body through x-ray.The thin region slice obtained through the rotation
of the scanner.The CT Scan localizes the tumor regions and defining its dimensions, morphology but it is
not sensitive for small pituitary tumors,brain stem tumors or low grade astrocytomas and MRI Scan
(Magnetic Resonance Imaging ) spreads rotating magnetic field inside the human body which alters the
changes inside the atomic nuclei and the information is simultaneously recorded to construct the three
dimensional image through its gradients and arbitrary orientation.It not affects the body system since it
passes non ionizing radiation [5]. The MRI specialization types range from DTI image which enables
diffusion to be measured in multiple directions to examine the connectivity of different regions in the
brain and FLAIR (Fluid Attenuated Inversion Recovery) technique used to reduce the fluid contents
inside the skull region through the pulse sequence of excited and inversion [12]. The MRI image slices
the input brain image into axial, coronal and sagittal for better enhancements are given in figure 1.4
(Courtesy to Aarthi Scans,Tirunelveli,India)
SAGITTAL VIEW AXIAL VIEW CORONAL VIEW
Fig 1.4 : MRI Scan is slicing the image in three planes
The gadolinium-based agent used to embellish the presence of soft tissues like neoplasm and blood
vessels to improve the visibility of internal structures for enhancement. It localizes the tumor and nearby
structures with a high-resolution image, it also diagnosis of sub-tentorial tumors, intra-axial tumors for
planning in three-dimensional imaging for pre-surgical analyzing. The DTI (Diffusion Tensor Imaging )
maintains the connection between the tumor border and white matter and the corresponding tumor
growth,regression are compared in the white matter while MRA (Magnetic Resonance Angiography)
establish the relationship between the tumor region and blood vessels through a contrast agent like
gadolinium to generate the signal of image in a single plane. The MRS (Magnetic Resonance
Spectroscopy) evaluates the biological information about the tumor depends on the metabolic activity and
http://en.wikipedia.org/wiki/Gadoliniumhttp://en.wikipedia.org/wiki/Gadolinium -
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biochemical information.It helps to differentiate the tumor active regions from the necrosis or dead
cells.Finally, the advance method of MRI which applied in neuro-surgical planning to localize the
temporal resolution of the image by the feedback mechanism.It spreads the BOLD (Blood-oxygen-level-
dependent) agent to map the neural activity in the spinal cord and the brain[6].The PET Scan ( Positron
Emission Tomography) introduces gamma rays emitted by positron tracer and it is an optional test to
gain further details about an MRI image through the level tumor absorbs the sugar content and it
differentiate scar tissue and recurring tumor cells and send the information to a computer which creates
live image.The PET scansacquired data through multiple ring detector and it coincidence the entire tissue
layer form two or three dimensional image [7].
V.TUMOR CLUSTERING
The brain tumor is classified in various grades from pilocytic astrocytoma as Grade I to GBM as Grade
IV and it's based on the aggressive form that spreads around the CSF region in the intracranial area of the
spinal cord.
Astrocytomas Oligodendrogliomas Ependymomas
Meningiomas Medulloblastomas Gangliogliomas
Schwannomas Craniopharyngiomas Chordomas
Fig 1.5 Various types of Anaplastic tumor of H&E Stain images.
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In pathology, the tumors are differentiated by the shape and orientation of cells through the popular
staining method in histology known as Hematoxylin-andEosin stain ( H&E Stain )The hemalum is the
mixture of aluminium ions, oxidized haematoxylin and immersion of an aqueous solution of rosin which
distinguish the color shade ranges from pink, red, blue and orange. The staining through H&E dye
extracts the microscopic structures present in the sample [9]. The primary tumor starts in the brain and
metastatic tumor spread to the brain from the other part of the body. The astrocytoma in grade III as
anaplastic astrocytoma which contains mix of cells and cells grades and having tentacle like projection in
a cluster form whereas the oligodendroglioma has fried egg cells with compact nuclei in its histological
appearance.Similarly,the ependymoma morphological appearance also different and composed with
regular oval nuceli in a elongated structures[1].The below H&E Stain of tumor image are the most basic
forms of tumor and its distribution are uneven and the pathologists differentiates the various types of
microscopic tissue.The following stained images of most common brain tumor tissues orientation in
different clustering form[9].
VI. IMPLEMENTATION
The stain image consists of multiple color patterns and the luminosity and chromaticity indicates variousdistinguish regions and the Euclidean distance metric in MATLAB helps to find the difference between
two color patterns.Then, the Clustering is a way to separate groups of objects and to avoid the local
minima the K-means clustering treats each object as having a location in space.
a b c Fig 1.6: a)Chordomas tumor cells in R&E Stain, b) Image labeled by cluster index, c) Object-1 cluster
d e f d) Object-2 cluster e) Object-3 cluster f) Segment the Nuceli into Separate Image.
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It finds partitions such that objects within each cluster are as close to each other as possible, and as far
from objects in other clusters as possible. K-means clustering requires that you specify the number of
clusters to be partitioned and a distance metric to quantify how close two objects are to each other.After
discriminating the cluster into three objects, a unique label identifier acquires to individual objects for
classification.Finally, the microscopic affected tissue is separated from the unaffected tissue region and
stain mark through segmenting the H&E image by color properties and extract the brightness values of
the pixels in the cluster and threshold them using im2bw in MATLAB and the processed figure are given
below
VII. Fuzzy Cluster Image Classification
The cluster image classification of spectral information involves soft computing method to classify the
objects in the tumor tissue cluster images. The soft computing is a flexible methodology that exploits the
fault tolerance for uncertainty through neural networks and fuzzy c-Means Algorithm based on the feature
resources available in the spectrum of images. The colors of various combinations that present in the
given cluster images are Blue (B), Green (G) and red (R). In spectrum measurement, these three color
variation extract the specific spectrum information from the object through the Euclidean distance metric
and through the HIS-Model hue (H) describes the pure color in terms of the dominant wavelength and it's
given by
1
12 2
1[(R G) ( )]
2cos[(R G) ( )( )]
}{ R BHR B G B
(1)
Again, the saturation (S) gives amplification of white color debasing the real color of the image and it
given by
3*min(R,G,B)1
( )R G Bs
(2)
Then, Intensity measure is the average of all combinations of different object color by [14]
I= 1
3R G B (3)
The usefulness of three color models was studied using data from computer simulations and experimental data from
an immune-double stained tissue section..Direct use of the three intensities obtained by a color camera results in the
red-green-blue (RGB) model. By decoupling the intensity of the RGB data, the hue-saturation-intensity (HSI) modelis obtained. However, the major part of the variation in perceived intensities in transmitted light microscopy is
caused by variations in staining density. Therefore, the hue-saturation-density (HSD) transform was defined as the
RGB to HSI transform, applied to optical density values rather than intensities for the individual RGB channels. The
HSD model enabled all possible distinctions in a two-dimensional, standardized data space [15].
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Astrocytomas Oligodendrogliomas Ependymomas
Meningiomas Medulloblastomas Gangliogliomas
Schwannomas Craniopharyngioma Chordomas
Fig 1.7 Object separation (affected tumor tissue) of Anaplastic tumor of H&E Stain images.
In the next stage, the fuzzy k-Means algorithm applied for finding the intra cluster distance though
minimizing the objective function which relevant data point for a set of prototypes:
2,
1 1
1( , )
2
mN c
FCM x i x i
X i
J z vd
(1)
Here, ,x i (x=1, 2,., N, i=1, 2, c) is membership value, it denotes fuzzy membership of data point x
belonging to class I, Vi(i=1, 2,, c) is centroid of each cluster and Zx(x=1,2,.,N) is data set (pixel values
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in image), m is fuzzification parameter d2(Zx, VI) is Euclidean distance between Zx and Vi, N is the number
of data points, C is the number of clusters.
Fuzzy partition is carried on an iterative optimization of the equation (1) based on [12]:
1) Choose primary centroids Vi (prototypes).
2) Computes the degree of membership of all data set in all the clusters:
(1 / m 1)2
,
(1 / m 1)2
1
1
( , )
1
( , )
)
( )
(x i
x ic
x ii
d z v
d z v
(2)
3) Compute New centroids V1i:
,
1
,
1
N
mx
x i
t x
i N
m
x i
x
z
V
(3)
and update the degree of membership ,x i according to the equation.
4) If ,x i
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REFERENCE
[1] Stupp, Roger, et al. "European Organization for Research and Treatment of Cancer Brain Tumor and
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[2]Dou, W., Ruan, S., Chen, Y., Bloyet, D., and Constans, J. M. (2007). A framework of fuzzy
information fusion for segmentation of brain tumor tissues on MR images. Image and Vision Computing,
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[3] Waxman, S. G. (1999). Correlative Neuroanatomy. McGraw-Hill, 24th edition.
[4] Jones, Edward G., and Lorne M. Mendell. "Assessing the decade of the brain."Science 284.5415
(1999): 739-739.
[5] Krupinski, Elizabeth. "The Handbook of Medical Image Perception and Techniques." Cambridge
2010
[6]Luechinger, Roger, et al. "Safety considerations for magnetic resonance imaging of pacemaker and
ICD patients."Herzschrittmachertherapie und Elektrophysiologie 15.1 (2004): 73-81.
[7] Valk, Peter E., et al., eds.Positron emission tomography: clinical practice. Springer, 2006.
[8] Surawicz, Tanya S., et al. "Brain tumor survival: results from the National Cancer Data
Base."Journal of neuro-oncology 40.2 (1998): 151-160.
[9] Wolf, Helmut K., et al. "Ganglioglioma: a detailed histopathological and immunohistochemicalanalysis of 61 cases."Acta neuropathologica 88.2 (1994): 166-173.
[10] McMaster, Jacqueline, Thomas Ng, and Mark Dexter. "Intraventricular rhabdoidmeningioma."Journal of clinical neuroscience 14.7 (2007): 672-675.
[11] Khan, Gulfaraz. "Epstein-Barr virus, cytokines, and inflammation: a cocktail for the pathogenesis of
Hodgkin's lymphoma?."Experimental hematology 34.4 (2006): 399-406.
[12]Kaghed, Nabeel Hashem, and Samaher Hussein Ali. "Generating Rules from Trained Neural Network
using FCM for Satellite Images Classification."
[13] Kaya, Metin. "Image Clustering and Compression Using An Annealed Fuzzy Hopfield Neural
Network."International Journal of Signal Processing1.2 (2005).
[14] Weeks, Arthur R., and G. Eric Hague. "Color segmentation in the HSI color space using the K-means algorithm."Electronic Imaging'97. International Society for Optics and Photonics, 1997.
[15] van der Laak, Jeroen AWM, et al. "Huesaturationdensity (HSD) model for stain recognition indigital images from transmitted light microscopy." Cytometry 39.4 (2000): 275-284.
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ACKNOWLEDGMENT
The authors are thankful to Dr. P. Suyambu, Chairman, PSN Group of
Institutions for his constant support,encouragement and his valuablesuggestion and motivation through financially for implementing theresources of the paper publications.His versatile knowledge andenthusiasmtowards multidisciplinaryresearch areas paved the way of a new foundation
to reach a milestone for younger generations research scholars to learn he is
the epitome of success.
AUTHORS
Ashish Chaturvedi is a Professor and Associate director of the Arni School of
Computer Science & Application at Arni University, Kathgarh ( Indora ), Himachal
Pradesh, India from August 2011 to till date and he worked in various posts like
Acting Director in Kishan Institute of Engineering & Technology, UP, India. He
worked as Head of the Department in the Department of Applied Sciences in Gyan
Bharti Institute of Technology from July 2008 to Feb 2011 and he has 14 years of
teaching experience in various educational Institutions. He completed his M. Tech in
Information Technology, India and other M. Tech by Research in 2012. He finished
his Ph.D ( Computer Science ) from Dr. B. R. Ambedkar University, UP, India in
2006.He is a reviewer of International Journal of Software Engineering and
Knowledge Engineering run by World Scientific Publishing Co., USA; IEEE Transaction of Fuzzy Systems;International Journal of Physical Sciences; International Journal of Computer Applications, USA. He is a patron
for series of International Scientific and Engineering Serial Journals includes International Journal of Interactive
Computer Communication (IJICC);International Journal of Emerging Science & Emerging Technology( IJESET
); International Journal of Pure & Applied Sciences ( IJPAS ) and International Journal of Arts Commerce
Management ( IJACM ). He is authoring two famous Books entitled Physics for Contemporary Engineers &
Components of Software Engineering with Galgotia Publications, New Delhi. Under his supervi sion for Ph.D in
Computer Science, there are 14 students awarded Ph.D Degree in Computer Science & Engineering on the research
related to Neural Network, Pattern Recognition and Medical Image processing. He selected and participated 6 times
in the prestigious short term courses conducted by Indian Institute of Technology Rookee, India. He published his
research paper in more than 70 International Journals including IEEE Publications. He presented and published
many research papers in International Conference and he attended more than 37 International Conference and many
National and International Seminars. His research articles entitled Consciousness in Quantum Brain published in
STANCE 2005. He gauges his research interest in applying artificial neural networks in various fields like Quantum
Physics, Medical Science, Computer Science and Geo-Science. Currently, he is exploring the fields like Neural
Networks, Fuzzy Logic, Genetic Algorithm, Pattern Recognition, Robotics, Image Processing, Quantum Physics,
Geo-Science, Medical Physics, and Graphical Authentication.
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Vijayakumar is a reseach scholar and he obtained his B.E in Computer Science and
Engineering, Sivanthi Aditanar College of Engineering and M.Tech in Computerand Information Technology in MS university in 2004 and he worked as professor
and Head of the Department of Computer Science and Engineering in various
engineering institutions for past 8 years.He is a member of Many Medical Imaging
Journals including BMC journals, SAGE Journals and Journa of the National Cancer
Institutue.He participated and proceeds in more than 20 national and international
conference and he published more than 15 international journals with impact factor
journals ( based on Thomson Reuters ).He currently pursuing his research in Bio-
Medical image computing and processing,Artifical intelligence, Neural Network and
fuzzy logic.
Muthu Kumar was born in Tirunelveli, India in 1989. He completed his Bachelor of
Information and Technology. He is currently doing M. Tech in Department of
Information Technology in PSN College of Engineering. He is a member of BMC
Journals, ACI Medical journals,SAGE journals and Journal of the National Cancer
Institute. He participated in many international conferences in various states and he
published various International Journals related to brain tumor image Computing. His
research interests primarily focus on image processing, especially in the methods
related to Biomedical image computing and processing, Robotic Surgery and
Hologram .