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Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI

K. Sarkar1 , R.K. Mandal2 , A. Mandal3 , S. Sarkar4

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-2 , Page no. 112-120, Feb-2017

Online published on Mar 01, 2017

Copyright © K. Sarkar, R.K. Mandal, A. Mandal , S. Sarkar . 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: K. Sarkar, R.K. Mandal, A. Mandal , S. Sarkar , “Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.112-120, 2017.

MLA Style Citation: K. Sarkar, R.K. Mandal, A. Mandal , S. Sarkar "Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI." International Journal of Computer Sciences and Engineering 5.2 (2017): 112-120.

APA Style Citation: K. Sarkar, R.K. Mandal, A. Mandal , S. Sarkar , (2017). Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI. International Journal of Computer Sciences and Engineering, 5(2), 112-120.

BibTex Style Citation:
@article{Sarkar_2017,
author = {K. Sarkar, R.K. Mandal, A. Mandal , S. Sarkar },
title = {Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2017},
volume = {5},
Issue = {2},
month = {2},
year = {2017},
issn = {2347-2693},
pages = {112-120},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1188},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1188
TI - Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI
T2 - International Journal of Computer Sciences and Engineering
AU - K. Sarkar, R.K. Mandal, A. Mandal , S. Sarkar
PY - 2017
DA - 2017/03/01
PB - IJCSE, Indore, INDIA
SP - 112-120
IS - 2
VL - 5
SN - 2347-2693
ER -

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Abstract

In recent years, Brain Tumor has become one of the most common deadly diseases and MRI is commonly used to diagnose it. Automated recognition of brain tumors from MRI is a difficult task because of the variability of size, shape, and contrast of the tumor. On the other hand, it has a huge impact in helping the physicians by assessing the type, size, exact topological location and other related parameters of the tumor. Image segmentation techniques are often applied in identifying the tumor from the MRI images in addition to other techniques. There are numerous segmentation techniques available for this purpose such as: (i) Region based (ii) Edge based (iii) Threshold based. Here a threshold based approach has been designed and proposed to do the segmentation of edema, where the threshold is determined by MAXNET, a Self Organization Map (SOM) based artificial neural network.

Key-Words / Index Term

Artificial Neural Network (ANN), Brain Tumor, Least centre distance method, Magnetic resonance imaging, MAXNET, segmentation, Self Organizing Map (SOM)

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