Application of Independent Component Analysis in MRI Classification
碩士 === 國立勤益科技大學 === 電子工程系 === 97 === Numerous scholars have submitted on the theory and research of Independent Component Analysis (ICA) in recent years. Although Independent Component Analysis (ICA) has been used in various fields, applying the AIS to medical images is vary rare. The purpose of thi...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2009
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| Online Access: | http://ndltd.ncl.edu.tw/handle/70710232795313816995 |
| Summary: | 碩士 === 國立勤益科技大學 === 電子工程系 === 97 === Numerous scholars have submitted on the theory and research of Independent Component Analysis (ICA) in recent years. Although Independent Component Analysis (ICA) has been used in various fields, applying the AIS to medical images is vary rare. The purpose of this study is using the Independent Component Analysis (ICA) by unsupervise for classifying the brain MRI, and display a single organism image which can finally offer faster organism reference information to a doctor and reduce the time to ascertain large number of images, so that the doctor can diagnose the nidus more efficiently and accurately. In order to verify the feasibility and efficiency of this method, we adopt statistical theory for manifold assessment and compare with the C-means and CEM. The result proves the method of this study is both feasible and useful.
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