Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki

Segmentation is in many cases the bottleneck when trying to use radiological image data in many clinically important applications as radiological diagnosis, monitoring, radiotherapy and surgical planning. While manual segmentation is often regarded as a gold standard, its usage is not acceptable in...

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Main Author: Ahmad Safwan, Mohamed Makki
Format: Thesis
Published: 2003
Subjects:
Online Access:http://studentsrepo.um.edu.my/10318/
http://studentsrepo.um.edu.my/10318/1/Ahmad_safwan_Mohamed_Makki.pdf
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author Ahmad Safwan, Mohamed Makki
author_facet Ahmad Safwan, Mohamed Makki
author_sort Ahmad Safwan, Mohamed Makki
building UM Research Repository
collection Online Access
description Segmentation is in many cases the bottleneck when trying to use radiological image data in many clinically important applications as radiological diagnosis, monitoring, radiotherapy and surgical planning. While manual segmentation is often regarded as a gold standard, its usage is not acceptable in some clinical situations. A new breed of methodologies improves on the shortcoming of the traditional methods. These new solution are describing as intelligent and encompass fuzzy logic and neural network. Fuzzy logic and Neural network are complimentary and can be combined to form a neuro-fuzzy approach. This overcomes the shortcoming of both and can provide a robust and intelligent methodology for image analysis. This documentation is about the implementation of neuro-fuzzy approach using Fuzzy Hopfield Neural Network (FHNN) algorithm for the segmentation process of MR image data sets.
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format Thesis
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institution University Malaya
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publishDate 2003
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spelling um-103182020-05-14T20:28:21Z Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki Ahmad Safwan, Mohamed Makki QA75 Electronic computers. Computer science QA76 Computer software Segmentation is in many cases the bottleneck when trying to use radiological image data in many clinically important applications as radiological diagnosis, monitoring, radiotherapy and surgical planning. While manual segmentation is often regarded as a gold standard, its usage is not acceptable in some clinical situations. A new breed of methodologies improves on the shortcoming of the traditional methods. These new solution are describing as intelligent and encompass fuzzy logic and neural network. Fuzzy logic and Neural network are complimentary and can be combined to form a neuro-fuzzy approach. This overcomes the shortcoming of both and can provide a robust and intelligent methodology for image analysis. This documentation is about the implementation of neuro-fuzzy approach using Fuzzy Hopfield Neural Network (FHNN) algorithm for the segmentation process of MR image data sets. 2003 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/10318/1/Ahmad_safwan_Mohamed_Makki.pdf Ahmad Safwan, Mohamed Makki (2003) Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki. Undergraduates thesis, University of Malaya. http://studentsrepo.um.edu.my/10318/
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Ahmad Safwan, Mohamed Makki
Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki
title Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki
title_full Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki
title_fullStr Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki
title_full_unstemmed Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki
title_short Mr image volume segmentation using neuro-fuzzy method / Ahmad Safwan Mohamed Makki
title_sort mr image volume segmentation using neuro-fuzzy method / ahmad safwan mohamed makki
topic QA75 Electronic computers. Computer science
QA76 Computer software
url http://studentsrepo.um.edu.my/10318/
http://studentsrepo.um.edu.my/10318/1/Ahmad_safwan_Mohamed_Makki.pdf