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Brain Tumor Detection and Segmentation Using Conditional Random Field

Vulavabeti Raghunath Reddy1 , Shaik Anusha2 , K Ravindra Reddy3

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 774-779, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.774779

Online published on Jan 31, 2019

Copyright © Vulavabeti Raghunath Reddy, Shaik Anusha, K Ravindra Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Vulavabeti Raghunath Reddy, Shaik Anusha, K Ravindra Reddy, “Brain Tumor Detection and Segmentation Using Conditional Random Field,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.774-779, 2019.

MLA Style Citation: Vulavabeti Raghunath Reddy, Shaik Anusha, K Ravindra Reddy "Brain Tumor Detection and Segmentation Using Conditional Random Field." International Journal of Computer Sciences and Engineering 7.1 (2019): 774-779.

APA Style Citation: Vulavabeti Raghunath Reddy, Shaik Anusha, K Ravindra Reddy, (2019). Brain Tumor Detection and Segmentation Using Conditional Random Field. International Journal of Computer Sciences and Engineering, 7(1), 774-779.

BibTex Style Citation:
@article{Reddy_2019,
author = {Vulavabeti Raghunath Reddy, Shaik Anusha, K Ravindra Reddy},
title = {Brain Tumor Detection and Segmentation Using Conditional Random Field},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {774-779},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3582},
doi = {https://doi.org/10.26438/ijcse/v7i1.774779}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.774779}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3582
TI - Brain Tumor Detection and Segmentation Using Conditional Random Field
T2 - International Journal of Computer Sciences and Engineering
AU - Vulavabeti Raghunath Reddy, Shaik Anusha, K Ravindra Reddy
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 774-779
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portions of the human body for medical diagnosis. MR images are widely used in the diagnosis of brain tumor. In this paper, we present an automated method to detect and segment the brain tumor regions. The proposed method consists of three main steps: initial segmentation, modeling of energy functions and optimize the energy function. To make our segmentation more reliable we use the information present in the T1 and FLAIR MRI images. We use Conditional random field (CRF) based framework to combined the information present in T1 and FLAIR in probabilistic domain. A main advantage of CRF based framework is we can model complex shapes easily and we incorporate the observations in energy function

Key-Words / Index Term

Conditional random field (CRF), Fuzzy-C-Means algorithm, Fuzzy C Means Clustering Algorithm

References

[1]. B. Thiagarajan, R. Bremananth “Brain Image Segmentation Using Conditional Random Field Based On Modified Artificial Bee ColonyOptimization Algorithm” International Journal of Biomedical and Biological Engineering vol ., Vol:8, No:9, 2014
[2]. M. Stella Atkins and Blair T. Mackiewich “Fully Automatic Segmentation of the Brain in MRI” IEEE Trans.on medical imaging vol .17, No .1,Feb 1998
[3]. B. Johnston, M. S. Atkins, B. Mackiewich, and M. Anderson, “Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI,” IEEE Trans. Med. Imag., vol. 15. pp. 154–169, Apr. 1996.
[4]. W. M. Wells,W. E. L. Grimson, R. Kikinis, and F. A. Jolesz, “Adaptive segmentation of MRI data,” IEEE Trans. Med. Imag., vol. 15, pp. 429–443, Aug. 1996.
[5]. D. R. Thedens, D. J. Skorton, and S. R. Fleagle, “Methods of graph searching for border detection in image sequences with applications to cardiac MRI,” IEEE Trans. Med. Imag., vol. 14, pp. 42–55, Mar. 1995
[6]. L. H. Staib and J. S. Duncan, “Boundary finding with parametrically deformale models,” IEEE Trans, Pattern Anal. Machine Intell., Nov. 1992.
[7]. S. Lobregt and M. Viergever, “A discrete dynamic contour model,” IEEE Trans. Med. Imag., vol. 14, pp. 12–24, Mar. 1995.
[8]. M. E. Brummer, R. M. Mersereau, R. L. Eisner, and R. R. J. Lewine, “Automatic detection of brain contours in MRI data sets,” IEEE Trans. Med. Imag., vol. 12, pp. 153–166, June 1993.
[9]. Dr. Saravana Kumar Ganesan and Dr. Ravi Sundaram3 et.al “An Automated Image Segmentation Scheme for MRI” International Journal of Advance Research in Computer Science and Management Studies vol .1,Issue .6, pp . 149-157, Nov 2013.
[10]. Alex P. Zijdenbos and Benoit M. Dawant et.al “Morphometric Analysis of White Matter Lesions in MR Images: Method and Validation” IEEE Trans on Medical imaging, vol .13, No .4, December 1994.
[11]. N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques for Image Segmentation-A Survey of Soft Computing Approaches”, International Journal of Recent Trends in Engineering, Vol.1, No.2, pp.250-254, May 2009.
[12]. Fauzia Khan, Shoaib A. Khan and Ubaid Ullah Yasin et.al” Detection of Glaucoma Using Retinal Fundus Images” IEEE Biomedical Engineering International Conference (BMEiCON-2013).
[13]. Saeid Fazli,, Parisa Nadirkhanlou” A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images”
[14]. Rupinder Kaur and Er. Garima Malik “An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF” Journal of Image and Graphics, vol .2, No .2, Dec 2014.
[15]. Samir Kumar Bandhyopadhyay, Tuhin Utsab Paul “Automatic Segmentation of Brain Tumour from Multiple Images of Brain MRI”(IJAIEM), vol .2, Issue 1, Jan 2013.
[16]. C. A. Davatzikos and J. L. Prince, “An active contour model for mapping the cortex,” IEEE Trans. Med. Imag., vol. 14, pp. 65–80, Mar. 1995.
[17]. Rehna Kalam, M. Abdul Rahman “Tumor and Edema Segmentation Using Efficient MFCM and MRG Algorithm” (IJRIIT), vol .3, Issue .3,pp .615, 2017.
[18]. Ajala Funmilola A, Oke O.A, Adedeji T.O, Alade O.M, and Adewusi E.A”Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation” Journal of Information Engineering and Applications. Vol .2, No .6, 2012.
[19]. V. Janani, P. Meena”Image Segmentation For Tumor Detection Using Fuzzy Inference System”, (IJCSMC), vol .2, Issue 5,pp.no .244- 248, May 2013.
[20]. Jibi Belghese, Sheeja Agustin “Brain Tumor Segmentation using Pattern Neural Networks with MRI Images”, IJSTE, vol .3, Issue .9, March2017
[21]. Nikhita Biradar, Prakash H. Unki “Brain Tumor Detection Using Clustering Algorithms in MRI Images”, (IRJET), vol .4, Issue 6, June 2016.
[22]. Priyadharsini.B “A Novel Noise Filtering Technique for Denoising MRI Images”, (IJIRCCE), vol .2, Issue 1, March 2014.
[23]. Guido Gerig,Olafkubler et.al “Nonliner Anisotropic Filtering of MRI Data” IEEE Trans.on Medical imaging, vol .11,No.2,pp .221-232 June 1992.
[24]. P.D.Yadav, Y.M.Patil “A Brain Tumor Detection Using K-Means, Fuzzy C Means and Watershed”(ICLTESHM-17), pp. 23,04 Feb 2017.
[25]. Silky Narang, Madan Lal and Navdeep Kanwal A Comparitive Survey Of Various Segmentation Techniques , (IJIRD), vol .2 Issue 5, May 2013