| dc.contributor.advisor | 蔡尚岳 | zh_TW |
| dc.contributor.advisor | Tsai, Shang-Yueh | en_US |
| dc.contributor.author (作者) | 吳柏賢 | zh_TW |
| dc.contributor.author (作者) | Wu, Po-Shien | en_US |
| dc.creator (作者) | 吳柏賢 | zh_TW |
| dc.creator (作者) | Wu, Po-Shien | en_US |
| dc.date (日期) | 2021 | en_US |
| dc.date.accessioned | 1-七月-2021 20:01:42 (UTC+8) | - |
| dc.date.available | 1-七月-2021 20:01:42 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-七月-2021 20:01:42 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0107755003 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/135984 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 應用物理研究所 | zh_TW |
| dc.description (描述) | 107755003 | zh_TW |
| dc.description.abstract (摘要) | 本論文旨在使用二維對位的方法下，以不同頭型切片作為模板，比較二維對位方法與三維對位方法之間的效果差異。再以切片厚度作為控制變因，使用不同厚度的切片進行對位，以判斷切片厚度對於對位的影響。對位完成後，將影像資料元素代入標準化相互資訊的公式，量化兩影像之間相似的程度，其後建立數值矩陣，計算各矩陣內元素的平均與標準差，比較各組數值之間差異，判斷使用二維對位方法時，其效果與三維對位的差距。 | zh_TW |
| dc.description.abstract (摘要) | In this paper, using the two-dimensional registration method to compare the effect differences between the two-dimensional registration method and the three-dimensional registration method via using different head slices.Thesection thicknessis used as the control variable, and different slice thicknessare used for registration to determine the effect of section thickness on registration.After the registrationcompleted, Substitute theelements of image datainto the Normalized mutual information formulato quantify the degree of similarity between the two images.Then establish the numerical matrix, calculating the average and standard deviation of the elements in each matrix, comparing the difference between the values of each group, and judging the difference between using three-dimensional registration methodand two-dimensional registration method. | en_US |
| dc.description.tableofcontents | 第一章 緒論 11.1 內容大綱 11.2 研究動機 11.3 研究目的 21.4 研究方法 2第二章 方法與步驟 32.1 三維全腦對位 32.2 二維對位 142.3 標準化相互資訊 222.4 建立NMI數值矩陣 27第三章 數據結果 283.1 取用與計算數值矩陣內元素 283.2 NMI數值矩陣之平均與標準差結果 29第四章 結論 34參考文獻 36附件 37附件A -二維對位 37附件B -NMI算法一 38附件C- NMI算法二 39 | zh_TW |
| dc.format.extent | 2846280 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0107755003 | en_US |
| dc.subject (關鍵詞) | 二維對位 | zh_TW |
| dc.subject (關鍵詞) | 三維對位 | zh_TW |
| dc.subject (關鍵詞) | 對位 | zh_TW |
| dc.subject (關鍵詞) | 相互資訊 | zh_TW |
| dc.subject (關鍵詞) | 標準化相互資訊 | zh_TW |
| dc.subject (關鍵詞) | Two-dimensional registration | en_US |
| dc.subject (關鍵詞) | Three-dimensional registration | en_US |
| dc.subject (關鍵詞) | Registration | en_US |
| dc.subject (關鍵詞) | Mutual information | en_US |
| dc.subject (關鍵詞) | Normalized mutual information | en_US |
| dc.title (題名) | 大腦核磁共振影像之二維對位比較 | zh_TW |
| dc.title (題名) | Two-dimensional image registration on MRSI metabolite concentration maps | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | [1]郭盈昇、陳啟仁、王宣惠、關婉君、蔡承坤、邱文達， “磁振造影於中樞神經系統之運用”，科儀新知，Vol.32，No.4，(2016)。[2]JohnKurhanewiczDaniel, B.VigneronSarah,and J.Nelsonm, “Three-Dimensional Magnetic Resonance Spectroscopic Imaging of Brain and Prostate Cancer1,” Neoplasia, Vol. 2, Issue1-2, pp.166-189(2000).[3]Senthil Periaswamy andHany Farid,“Elastic Registration in the Presence ofIntensity Variations,” IEEE Transactions on Medical Imaging, Vol.22,No.7(2003).[4]Senthil Periaswamy and Hany Farid,“Medical Image Registration with Partial Data,”Medical Image Analysis, Vol. 10,Issue 10, pp.452-464(2006).[5]Thomas M. Coverand Joy A. Thomas, “Elements of Information Theory,” JOHN WILEY & SONS, New Jersey,2006.[6] La The Vinh, Sungyoung Lee, Young-Tack Park, and Brian J. d’Auriol, “A novel feature selection method based on normalized mutual information,” Applied Intelligence, Vol. 37, pp.100-120(2012). | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/NCCU202100531 | en_US |
