Cite Article

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

Choose citation format

BibTeX

@article{IJASEIT1271,
   author = {Rupika Nilakant and Hema P Menon and Vikram K},
   title = {A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1448--1456},
   keywords = {Magnetic Resonance Imaging (MRI); Brain Image; Segmentation; Neural Networks; Deformable Models; Fuzzy C-Means.},
   abstract = {

This paper presents a survey of advanced methods in segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in segmentation of each algorithm have been reported accordingly in the observations.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1271},    doi = {10.18517/ijaseit.7.4.1271} }

EndNote

%A Nilakant, Rupika
%A Menon, Hema P
%A K, Vikram
%D 2017
%T A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation
%B 2017
%9 Magnetic Resonance Imaging (MRI); Brain Image; Segmentation; Neural Networks; Deformable Models; Fuzzy C-Means.
%! A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation
%K Magnetic Resonance Imaging (MRI); Brain Image; Segmentation; Neural Networks; Deformable Models; Fuzzy C-Means.
%X 

This paper presents a survey of advanced methods in segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in segmentation of each algorithm have been reported accordingly in the observations.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1271 %R doi:10.18517/ijaseit.7.4.1271 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 4 %@ 2088-5334

IEEE

Rupika Nilakant,Hema P Menon and Vikram K,"A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1448-1456, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.1271.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Nilakant, Rupika
AU  - Menon, Hema P
AU  - K, Vikram
PY  - 2017
TI  - A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1448
EP  - 1456
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Magnetic Resonance Imaging (MRI); Brain Image; Segmentation; Neural Networks; Deformable Models; Fuzzy C-Means.
N2  - 

This paper presents a survey of advanced methods in segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in segmentation of each algorithm have been reported accordingly in the observations.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1271 DO - 10.18517/ijaseit.7.4.1271

RefWorks

RT Journal Article
ID 1271
A1 Nilakant, Rupika
A1 Menon, Hema P
A1 K, Vikram
T1 A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1448
OP 1456
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Magnetic Resonance Imaging (MRI); Brain Image; Segmentation; Neural Networks; Deformable Models; Fuzzy C-Means.
AB 

This paper presents a survey of advanced methods in segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in segmentation of each algorithm have been reported accordingly in the observations.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1271 DO - 10.18517/ijaseit.7.4.1271