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Reseach Article

Efficient Algorithm for the Detection of a Brain Tumor from an MRI Images

by Ammar A. Radhi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 10
Year of Publication: 2017
Authors: Ammar A. Radhi
10.5120/ijca2017912990

Ammar A. Radhi . Efficient Algorithm for the Detection of a Brain Tumor from an MRI Images. International Journal of Computer Applications. 170, 10 ( Jul 2017), 38-42. DOI=10.5120/ijca2017912990

@article{ 10.5120/ijca2017912990,
author = { Ammar A. Radhi },
title = { Efficient Algorithm for the Detection of a Brain Tumor from an MRI Images },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 170 },
number = { 10 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume170/number10/28118-2017912990/ },
doi = { 10.5120/ijca2017912990 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:18:09.684972+05:30
%A Ammar A. Radhi
%T Efficient Algorithm for the Detection of a Brain Tumor from an MRI Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 170
%N 10
%P 38-42
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical image processing is considered as a very promising field due to its role in medical diagnoses of fatal diseases like cancers, with the availability of the advanced technology, detection of tumours has become easier nowadays. X-ray images and MRI images are examples which help in the earlier detection of different kind of tumours. However, further enhancement of these methods is currently undertaken. In this paper an algorithm for accurate detection of brain tumours is proposed, and based on the result obtained, it gives more accurate detection of a brain tumour in comparison to other methods like K-cluster, watershed algorithm, and threshold selection method. The algorithm is based on the application of a specific formula that segments the image very efficiently and isolates the tumour from the skull and other brain tissues based on the solidity and area. A comparison of the result of this algorithm with the previously mentioned methods is also proposed.

References
  1. Dr.K.Sakthivel, B.R.Swathi, S.VishnuPriyan, C.Yokesh: ‘Analysis of Medical Image Processing and its Application in Healthcare’2016.
  2. Bernd Girod: ‘Morphological Image Processing’ 2013 Stanford University.
  3. Rohini Paul Joseph, C. Senthil Singh, M.Manikandan:‘Brain Tumor MRI Image Segmentation and Detection in Image Processing’ 2014.
  4. Nasir Ahmed, Kamisetty RamamohanRao: ‘Feature Selection in Pattern Recognition’, ‘Orthogonal Transforms for Digital Signal Processing’ Springer-Verlag , New York 1975
  5. Miss. Roopali R. Laddha, Dr.Siddharth A. Ladhake:‘Brain Tumor Detection Using Morphological and Watershed Operator’ 2014
  6. Dr.RashiAgarwal:‘Watershed Algorithm for Segmentation’ 2015.
  7. Nobuyuki Otsu: ‘A threshold Selection Method from Gray Level Histograms’ 1979.
  8. Ahmad Dahlan, Prof.Soepomo Street, Janturan, Yogyakarta:‘Image Enhancement Using Contrast Stretching on RGB and IHS Digital Image’ 2007..
  9. ‘Angel Johncy: Extraction of Connected Component without using BWLABEL in image processing’http://angeljohnsy.blogspot.com/2012/03/extraction-of-connected-components.html.accessedOctober 31, 2014
  10. C.P. Loizou, V. Murray, M.S. Pattichis, I. Seimenis, M. Pantziaris, C.S. Pattichis: ‘Multi-scale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis in brain MRI images’. 2011.
  11. C.P. Loizou, E.C. Kyriacou, I. Seimenis, M. Pantziaris, S. Petroudi, M. Karaolis, C.S. Pattichis:‘Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability’ 2013.
  12. C.P. Loizou, M. Pantziaris, C.S. Pattichis, I. Seimenis:‘Brain MRI Image normalization in texture analysis of multiple sclerosis’. 2013.
  13. C.P. Loizou, S. Petroudi, I. Seimenis, M. Pantziaris, C.S. Pattichis: ‘Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome’. 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Enhancement Segmentation Morphological Operation dilation filtering