Application of Grey System Theory in MRI Classification

碩士 === 國立勤益科技大學 === 電子工程系 === 97 === Magnetic Resonance Image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, Magnetic Resonance Image (MRI) makes it possible to generate several images on the same section....

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Bibliographic Details
Main Authors: Chun-Chia Hsieh, 謝俊嘉
Other Authors: Chuin-Mu Wang
Format: Others
Language:zh-TW
Published: 2009
Online Access:http://ndltd.ncl.edu.tw/handle/45772304534534309246
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Summary:碩士 === 國立勤益科技大學 === 電子工程系 === 97 === Magnetic Resonance Image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, Magnetic Resonance Image (MRI) makes it possible to generate several images on the same section. Despite of accessible abundant information, MRI also makes it more difficult to judge the location of every tissue. Moreover, Magnetic Resonance Image (MRI) will complicate the judgment due to strong noise. In order to resolve this problem, this paper endeavors to classify them via the help of Grey System Theory , This study has to demonstrate the advantages of Grey System Theory, statistical theory is considered as a judgment method, whereby obtaining experimental data of Extension Neural Network, Extension, FCM (Fuzzy c-means) and Perceptron for subsequent comparison. It has thus proved that Extension is superior to the other algorithms in terms of classification