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Brain Tumor Segmentation of MRI Images using Joint Techniques of Watershed and Morphological Operations

Published on February 2017 by Ashima Anand, Harpreet Kaur
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National Conference on Latest Initiatives and Innovations in Communication and Electronics
Foundation of Computer Science USA
IICE2016 - Number 2
February 2017
Authors: Ashima Anand, Harpreet Kaur
79e55e0e-5388-4639-b535-e70156d9e0c5

Ashima Anand, Harpreet Kaur . Brain Tumor Segmentation of MRI Images using Joint Techniques of Watershed and Morphological Operations. National Conference on Latest Initiatives and Innovations in Communication and Electronics. IICE2016, 2 (February 2017), 10-12.

@article{
author = { Ashima Anand, Harpreet Kaur },
title = { Brain Tumor Segmentation of MRI Images using Joint Techniques of Watershed and Morphological Operations },
journal = { National Conference on Latest Initiatives and Innovations in Communication and Electronics },
issue_date = { February 2017 },
volume = { IICE2016 },
number = { 2 },
month = { February },
year = { 2017 },
issn = 0975-8887,
pages = { 10-12 },
numpages = 3,
url = { /proceedings/iice2016/number2/26956-1683/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Latest Initiatives and Innovations in Communication and Electronics
%A Ashima Anand
%A Harpreet Kaur
%T Brain Tumor Segmentation of MRI Images using Joint Techniques of Watershed and Morphological Operations
%J National Conference on Latest Initiatives and Innovations in Communication and Electronics
%@ 0975-8887
%V IICE2016
%N 2
%P 10-12
%D 2017
%I International Journal of Computer Applications
Abstract

Segmentation of brain tumor is an example of medical image segmentation that has grown as an emerging area of research in magnetic resonance imaging(MRI). In biomedical imaging, accurate detection of tumor is utmost important for proper clinical practice and treatment. Several techniques have been proposed for brain tumor segmentation, but there is no perfect algorithm proposed yet to enhance tumor. Brain tumor MRI images display complicated features in appearance and boundaries. To eradicate this problem,novel methods are proposed for accurately segmentating the brain image. This research paper focus on the segmentation and detection of brain tumor using watershed and morphological operations. Results are evaluated with the implementation of the work carried out in MATLAB. In the end, conclusion and future aspects are addressed regarding brain tumor segmentation.

References
  1. Oelze, M. L,Zachary, J. F. , O'Brien, W. D. , Jr. , ?Differentiation of tumor types in vivo by scatterer property estimates and parametric images using ultrasound backscatter ? , on page(s) :1014 - 1017 Vol. 1, 5-8 Oct. 2003.
  2. D. D. Langleben and G. M. Segall, "PET in differentiation of recurrent brain tumor from radiation injury," J. Nucl. Med. , vol. 41, pp:1861–1867,2000.
  3. Ashima Anand,Harpreet Kaur "Survey on Segmentation of Brain Tumor: A Review of Literature", " International Journal of Advanced Research in Computer and Communication Engineering,Vol 5,Issue 1,January 2016.
  4. M. S. Atkins and B. T. Mackiewich, 1 J. C. Bezdek, " Fully Automatic segmentation of the brain in MRI", IEEE T. Med Imag,Issue 17,pp:98–109.
  5. H. D. Cheng, Y. H. Chen, and X. H. Jiang, "Thresholding using two dimensional histogram and fuzzy entropy principle", IEEE Trans. Image Processing, vol. 9, pp. 732-735, 2000.
  6. B. N. Saha, N. Ray, R. Greiner,A. Murtha, andH. Zhang, "Quick detection of brain tumors and edemas: A bounding box method using symmetry,"Comput. Med. Imag. Graphics, vol. 36, no. 2, pp. 95–107, 2012.
  7. J. Jiang, Y. Wu, M. Huang, W. Yang, W. F. Chen, and Q. J. Feng, "3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets," Comput. Med. Imag. Graphics, vol. 37, no. 7–8, pp. 512–521, Jun. 28, 2013.
  8. A. Hamamci, N. Kucuk, K. Karaman, K. Engin, and G. Unal, "Tumor- Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications," IEEE Trans. Med. Imag. , vol. 31, no. 3, pp. 790–804, Mar. 2012.
  9. Ayse Demirhan, Mustafa Toru, and Inan Guler, "Segmentation of Tumor and Edema Along with Healthy Tissues of Brain Using Wavelets and Neural Networks " ,IEEE Journal of biomedical and health informatics, vol. 19, no. 4, July 2015.
  10. Shafarenko, L. , Petrou, M. , Kittler, J. : Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing 6(11), 1530–1544 (1997).
  11. Gies V, Bernard T. Statistical solution to watershed over-segmentation. Int Conf Image Process; 2004. p. 1863–6.
Index Terms

Computer Science
Information Sciences

Keywords

Brain Tumor Segmentation Mr Images Watershed Algorithm Close Operation.