Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation

Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation

This paper studies the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of brain abnormality in MRI images. Segmentation of MRI image is an important part of brain imaging research. In this study, 150 MRI images were used as testing data for the system. The data was...

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Published in:Proceedings - ICSGRC 2010: 2010 IEEE Control and System Graduate Research Colloquium
Main Author: Noor N.M.; Khalid N.E.A.; Hassan R.; Ibrahim S.; Yassin I.M.
Format: Conference paper
Language:English
Published: 2010
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-77958017248&doi=10.1109%2fICSGRC.2010.5562519&partnerID=40&md5=a8ad434e7ffbec2503423fa147b6eda1
id 2-s2.0-77958017248
spelling 2-s2.0-77958017248
Noor N.M.; Khalid N.E.A.; Hassan R.; Ibrahim S.; Yassin I.M.
Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
2010
Proceedings - ICSGRC 2010: 2010 IEEE Control and System Graduate Research Colloquium


10.1109/ICSGRC.2010.5562519
https://www.scopus.com/inward/record.uri?eid=2-s2.0-77958017248&doi=10.1109%2fICSGRC.2010.5562519&partnerID=40&md5=a8ad434e7ffbec2503423fa147b6eda1
This paper studies the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of brain abnormality in MRI images. Segmentation of MRI image is an important part of brain imaging research. In this study, 150 MRI images were used as testing data for the system. The data was created by combining the shapes and size of various abnormalities and pasting it onto normal brain image. Several types of backgrounds were tested - low, medium and high grey levels. The experimental results show good segmentation for medium and low background levels value for both light and dark abnormality levels over different backgrounds. © 2010 IEEE.


English
Conference paper
author Noor N.M.; Khalid N.E.A.; Hassan R.; Ibrahim S.; Yassin I.M.
spellingShingle Noor N.M.; Khalid N.E.A.; Hassan R.; Ibrahim S.; Yassin I.M.
Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
author_facet Noor N.M.; Khalid N.E.A.; Hassan R.; Ibrahim S.; Yassin I.M.
author_sort Noor N.M.; Khalid N.E.A.; Hassan R.; Ibrahim S.; Yassin I.M.
title Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
title_short Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
title_full Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
title_fullStr Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
title_full_unstemmed Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
title_sort Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
publishDate 2010
container_title Proceedings - ICSGRC 2010: 2010 IEEE Control and System Graduate Research Colloquium
container_volume
container_issue
doi_str_mv 10.1109/ICSGRC.2010.5562519
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-77958017248&doi=10.1109%2fICSGRC.2010.5562519&partnerID=40&md5=a8ad434e7ffbec2503423fa147b6eda1
description This paper studies the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of brain abnormality in MRI images. Segmentation of MRI image is an important part of brain imaging research. In this study, 150 MRI images were used as testing data for the system. The data was created by combining the shapes and size of various abnormalities and pasting it onto normal brain image. Several types of backgrounds were tested - low, medium and high grey levels. The experimental results show good segmentation for medium and low background levels value for both light and dark abnormality levels over different backgrounds. © 2010 IEEE.
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language English
format Conference paper
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record_format scopus
collection Scopus
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