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Published in:

Volume 7 Issue 6
June-2020
eISSN: 2349-5162

Unique Identifier

JETIR2006195

Page Number

1337-1341

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Title

A Survey on Improved Brain Tumor Segmentation and Detection Using EM and FCM Algorithm

ISSN

2349-5162

Cite This Article

"A Survey on Improved Brain Tumor Segmentation and Detection Using EM and FCM Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.1337-1341, June-2020, Available :http://www.jetir.org/papers/JETIR2006195.pdf

Abstract

This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images and predicts the disease risk details from the given area of tumor. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. After researching a lot statistical analysis which is based on those people whose are affected in brain tumor some general Risk factors and Symptoms have been discovered. The development of technology in science day night tries to develop new methods of treatment. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method accurate determines the accurate of stage & size of tumor and also predicts the disease details from the area of tumor. This work uses segmentation of brain tumor based on the k-means and fuzzy c-means algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis and predicts the disease details from the given area of tumor. Finally implement a system using java to predict Brain tumor risk level which is easier, cost reducible and time savable.

Key Words

Abnormalities, Magnetic Resonance Imaging (MRI), Brain tumor, Pre-processing, fuzzy c-means, Thresholding.

Cite This Article

"A Survey on Improved Brain Tumor Segmentation and Detection Using EM and FCM Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1337-1341, June-2020, Available at : http://www.jetir.org/papers/JETIR2006195.pdf

Publication Details

Published Paper ID: JETIR2006195
Registration ID: 234197
Published In: Volume 7 | Issue 6 | Year June-2020
DOI (Digital Object Identifier):
Page No: 1337-1341
ISSN Number: 2349-5162

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Cite This Article

"A Survey on Improved Brain Tumor Segmentation and Detection Using EM and FCM Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1337-1341, June-2020, Available at : http://www.jetir.org/papers/JETIR2006195.pdf




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