Image processing algorithms for medical applications

Last few years has witnessed exponential growth in neuro-scientific field, especially for brain connectivity. Brain connectivity is primarily classified into three - structural connectivity, functional connectivity and effective connectivity. The advancement of neuroimaging techniques like fMRI has...

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Main Author: Naseef Abdul Kareem
Other Authors: Mohammed Yakoob Siyal
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/62074
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-620742023-07-07T16:04:22Z Image processing algorithms for medical applications Naseef Abdul Kareem Mohammed Yakoob Siyal School of Electrical and Electronic Engineering DRNTU::Engineering Last few years has witnessed exponential growth in neuro-scientific field, especially for brain connectivity. Brain connectivity is primarily classified into three - structural connectivity, functional connectivity and effective connectivity. The advancement of neuroimaging techniques like fMRI has accelerated the research pace. Among them, resting state fMRI has been gaining more momentum. Several observation and default mode network in the ‘task absent’ has caught the attention of scientists. As for analysis, time series extraction and graphical model representation methods are the most popular. Time series extraction gives a platform to apply different mathematical algorithms that have also been in use in other fields. Same time graphical representation summaries the global and regional variance into biologically meaningful properties. In this Final Year Project, a study on Functional connectivity analysis and Effective connectivity analysis were carried out. From the functional connectivity analyses, it has been observed that resting state connectivity of the brain is forming a Default Mode Network. Bachelor of Engineering 2015-01-10T06:35:11Z 2015-01-10T06:35:11Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/62074 en Nanyang Technological University 47 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Naseef Abdul Kareem
Image processing algorithms for medical applications
description Last few years has witnessed exponential growth in neuro-scientific field, especially for brain connectivity. Brain connectivity is primarily classified into three - structural connectivity, functional connectivity and effective connectivity. The advancement of neuroimaging techniques like fMRI has accelerated the research pace. Among them, resting state fMRI has been gaining more momentum. Several observation and default mode network in the ‘task absent’ has caught the attention of scientists. As for analysis, time series extraction and graphical model representation methods are the most popular. Time series extraction gives a platform to apply different mathematical algorithms that have also been in use in other fields. Same time graphical representation summaries the global and regional variance into biologically meaningful properties. In this Final Year Project, a study on Functional connectivity analysis and Effective connectivity analysis were carried out. From the functional connectivity analyses, it has been observed that resting state connectivity of the brain is forming a Default Mode Network.
author2 Mohammed Yakoob Siyal
author_facet Mohammed Yakoob Siyal
Naseef Abdul Kareem
format Final Year Project
author Naseef Abdul Kareem
author_sort Naseef Abdul Kareem
title Image processing algorithms for medical applications
title_short Image processing algorithms for medical applications
title_full Image processing algorithms for medical applications
title_fullStr Image processing algorithms for medical applications
title_full_unstemmed Image processing algorithms for medical applications
title_sort image processing algorithms for medical applications
publishDate 2015
url http://hdl.handle.net/10356/62074
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