Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

November Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the November 2021 Edition of the journal. The last date of research paper submission is October 15, 2021.

An Automatic Brain Tumor Detection and Segmentation using Hybrid Method

Sreedhanya S., Chhaya S. Pawar

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Sreedhanya S., Chhaya S. Pawar
10.5120/ijais2017451641
Download full text
  1. Sreedhanya S. and Chhaya S Pawar. An Automatic Brain Tumor Detection and Segmentation using Hybrid Method. International Journal of Applied Information Systems 11(9):6-11, January 2017. URL, DOI BibTeX

    @article{10.5120/ijais2017451641,
    	author = "Sreedhanya S. and Chhaya S. Pawar",
    	title = "An Automatic Brain Tumor Detection and Segmentation using Hybrid Method",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "January 2017",
    	volume = 11,
    	number = 9,
    	month = "Jan",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "6-11",
    	numpages = 6,
    	url = "http://www.ijais.org/archives/volume11/number9/962-2017451641",
    	doi = "10.5120/ijais2017451641",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

In the field of medical image processing, brain tumor detection and segmentation using MRI scan has become one of the most important and challenging research areas. In which manual detection and segmentation of brain tumors using brain MRI scan forms a large part of human intervention for detection and segmentation taken per patient, is both tedious and has huge internal and external observer detection and segmentation variability. Hence, there is high demand for an automatic brain tumor detection and segmentation using brain MR images to overcome manual segmentation. So in current days a number of methods have proposed by researchers. But still there is no complete automated system developed yet, is due to accuracy and robustness issues. So, this paper provides a review of the methods and techniques that used to detect and segment brain tumor through MRI segmentation. Finally, the paper concludes with one of the efficient hybrid method which shows high accuracy on detection of brain tumor with proposed Gaussian Mixture Model (GMM).

Reference

  1. Automated Brain Tumor Segmentation Using Region Growing Algorithm by Extracting Feature Volume 3, Issue 12, December 2014, ShraddhaP. Dhumal1and Ashwini S Gaikwad.
  2. Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications,27th March 2015 “Brain Tumor Detection and Identification Using K-Means Clustering Technique“ Malathi R Department of Computer Science.
  3. Preetha, R., and G. R. Suresh. "Performance Analysis of Fuzzy C Means Algorithm in Automated Detection of Brain Tumor." In Computing and Communication Technologies (WCCCT), 2014 World Congress on, pp. 3033. IEEE, 2014.
  4. Abdel-Maksoud, Eman, Mohammed Elmogy, and Rashid Al-Awadi. "Brain tumor segmentation based on a hybrid clustering technique." Egyptian Informatics Journal (2015)
  5. Yehualashet Megersa, Electrical and Computer Engineering Department, Addis Ababa University, “Brain Tumour Detection and segmentation using Hybrid Intelligent Algorithm”, November 2015.
  6. “Image Contrast Enhancement Using Guassian Mixture Modeling And Its Comparison With Different Algorithms” Sabahat Fatima1, Dr. Shubhang and Hashmath Fatima3.International Journal Of Advancement In Engineering Technology,Volume 3, Issue 2 May 2016.
  7. Dvorak, Pavel, Walter Kropatsch, and Karel Bartusek. "Automatic detection of brain tumors in MR images."In Telecommunications and Signal Processing (TSP), 2013 36th International Conference on, pp. 577-580. IEEE, 2013.
  8. Parisot,Sarah,HuguesDuffau,StéphaneChemouny, and Nikos Paragios."Graph-based detection, segmentation & characterization of brain tumors." In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 988-995. IEEE, 2012
  9. Sonu suhag, L. M. SAINI, “Automatic Detection Of Brain Tumor By Image Processing In Matlab”, SARC-IRF international conference, 24th may-2015
  10. Dvorak, Pavel, Walter Kropatsch, and Karel Bartusek. "Automatic detection of brain tumors in MR images." In Telecommunications and Signal Processing (TSP), 2013 36th International Conference on, pp. 577-580. IEEE, 2013.
  11. Ferlay J, Shin HR, Bray F, Forman D, Mathers C and Parkin DM, ‘GLOBOCAN 2008 v2.0, Cancer Incidence and Mortality Worldwide’, International Agency for Research on Cancer, Lyon, France, 2010.

Keywords

Brain Tumor, MRI, Tumor Segmentation and Detection, FHNN, GMM