Categorizing Brain Tumor in MRI using Digital Image Processing
Abstract
A brain Tumor is an abnormal cell development within the brain tissues.
Brain tumor can be categorized into two types as follows: Benign,
without cancer cells, Malignant, with cancer cells that develop quickly.
About 12% of worldwide deaths are caused because of brain tumor.
Tumor identification itself involves a number of complex methods, as
it requires a great deal of radiological knowledge and experience for
accurate medical image detection. Mechanization of tumor detection
is therefore needed, as there might be an unavailability of trained
radiologists at the time of great need.
Here we have proposed a system for programmed cerebrum tumor
discovery, i.e., pre-preparing, division and arrangement utilizing clinical
imaging consequences of attractive reverberation imaging (MRI). In the
principal stage, the MRI cerebrum picture is absorbed from patient’s
information base, commotion is taken out and the picture is sifted
utilizing different channels, which help in upgrading the highlights of
the picture. In the subsequent stage, we segment the image and then
increase the intensity of the pixels on the detected part of the image
using k-means algorithm. It makes the MRI image more reliable at a
rational processing time. The proposed method successfully recognizes
and segments the tumor portions of the images successfully.
How to cite this article:
AC Vikramathithan, NS Vasudha, Hegde KSS et
al. Categorizing Brain Tumorin MRI using Digital
Image Processing. J Adv Res Image Proc Appl
2020; 3(2): 10-16
References
Image. International Journal of Trend in Scientific
Research and Development (IJTSRD) 2019; 3(5).
2. Kumar P, Kumar BV. Brain Tumor MRI Segmentation and
Classification Using Ensemble Classifier. International
Journal of Recent Technology and Engineering (IJRTE)
2019; 8(1&4). ISSN; 2277-3878.
3. Rajiv S, kumar PR. Brain tumour MRI Segmentation and
Classification Using Ensemble Classifier. International
Journal of Mechanical Engineering and Technology
(IJMET) 2017; 8(10).
4. Parmar S, Gondaliya N. A Survey on Detection and
Classification of Brain Tumor from MRI Brain Images
using Image Processing Techniques. International
Research Journal of Engineering and Technology (IRJET)
2018; 5(2).
5. Mukambika PS, Uma RK. Segmentation and Classification
of MRI Brain Tumor. International Research Journal of
Engineering and Technology (IRJET) 2017; 4(7).
Figure 10.Classified Output of the MRI Image of a
Benign Brain Tumor
6. Jagan A. A New Approach for Segmentation and
Detection of Brain Tumor in 3D Brain MR Imaging.
Proceedings of the 2nd International conference on
Electronics, Communication and Aerospace Technology
(ICECA) IEEE Conference Record 2018; 42487. ISBN;
978-1-5386-0965-1.
7. Balakumar B, Muthukumar S, Raviraj P et al. An
Automatic Brain Tumour Detection and Segmentation
Scheme. International Journal of Emerging Technologies
in Computational and Applied Sciences (IJETCAS) 2014;
14-313.
8. Sudharson M, Thangadurai RSR, Ilavarasi PU. Brain
Tumor Detection by Image Processing using MATLAB.
Middle-East Journal of Scientific Research 2016; 24(S1):
143-148. ISSN; 1990-9233.
9. Mathew RA, Babu, Thara NK. Brain Tumor Segmentation
and Classification using DWT, Gabour Wavelet and
GLCM. International Conference on Intelligent
Computing, Instrumentation and Control Technologies
(ICICICT) 2017. ISBN; 978-1-5090-6106.
10. Anithaand SV, Murugavalli. Brain tumour classification
using two tier classifier with adaptive segmentationtechnique. IET Comput and The Institution of Engineering and Technology IEEE 2015.
11. Lavanyadevi R, Machakowsalya M, Nivethitha J et al.
Brain Tumor Classification and Segmentation in MRI
Images using PNN. International Conference on Electrical, Instrumentation and Communication Engineering
(ICEICE) IEEE 2017.
12. Khan IU, Akhter S, Khan S. Detection and Classification
of Brain Tumor using Support Vector Machine Based
GUI. 7th International Conference on Signal Processing
and Integrated Networks IEEE (SPIN) 2020. 978-1-
7281-5475.
