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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張璞曾 | |
dc.contributor.author | Hung-Chun Huang | en |
dc.contributor.author | 黃鴻鈞 | zh_TW |
dc.date.accessioned | 2021-06-13T16:27:17Z | - |
dc.date.available | 2010-07-26 | |
dc.date.copyright | 2005-07-26 | |
dc.date.issued | 2005 | |
dc.date.submitted | 2005-07-14 | |
dc.identifier.citation | 1. Torsten B.Moeller, Emil Reif, ”Pocket Atlas of sectional anatomy CT and MRI Volume 2”
Thieme,2002 2. 張斐章、張麗秋、黃浩倫,”類神經網路理論與實務” 東華書局,2003,ISBN 957-483-220-1 3. 葉怡成,”類神經網路模式應用與實作” 儒林,2002,ISBN 957-499-313-2 4. 羅華強,”類神經網路_Matlab的應用” 清蔚科技,2002,ISBN 957-97544-7-0 5. Sung-Bae Cho; ”Neural-Network Classifiers for Recognnizing Totally Unconstrained Handdwritten Numerals”, IEEE Transaction on Neural Network, Vol.8, No.1, January 1997, Page(s):43 - 53 6. Miyanaga, Y.; Hong Lan Jin; Islam, R.; Tochinai, K.; ”A self-organized network with a supervised training” Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on Volume 1, 28 April-3 May 1995 Page(s):482 - 485 vol.1 7. Jing Wu; Hong Yan; ”Combined SOM and LVQ Based Classifiers for Handwritten Digit Recognition” Neural Networks, 1995. Proceedings., IEEE International Conference on Volume 6, 27 Nov.-1 Dec. 1995 Page(s):3074 - 3077 vol.6 8. Baig, M.H.; Rasool, A.; Bhatti, M.I.; “Classification of electrocardiogram using SOM, LVQ and beat detection methods in localization of cardiac arrhythmias” Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE , Volume: 2 , 25-28 Oct. 2001, Page(s):1684 - 1687 9. 繆紹綱,”數位影像處理” 台灣培生教育,2003,ISBN 986-7594-11-8 10. 繆紹綱,”數位影像處理活用matlab” 全華,2003,ISBN 957-21-2467-6 11. Rafael C.Gonzalez, Richard E.Woods, Steven L.Eddins, ”Digital Image using Matlab Processing” Pearson Prentice Hall,2004 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38164 | - |
dc.description.abstract | 血管攝影方式目前分成侵入式(invasive)和非侵入式(non-invasive)兩種,而磁振血管造影(magnetic resonance angiography, MRA)具備非侵入式的優點,所以較易為人所接受,目前臨床上患者多透過MRA來做為血管診斷的先期篩檢。由於國人飲食生活的改變,罹患糖尿病的比例有增高的趨勢,而糖尿病患者多伴隨有下肢周邊血管動脈阻塞的現象,所以透過MRA我們可以清楚瞭解糖尿病患者下肢動脈形態,自主幹至分枝由粗而細,狹窄栓塞及阻塞之處。本論文則是針對MRA之下肢膝部動脈血管影像,嘗試利用二維結構之SOM(Self-Organizing Map)、LVQ(Learning Vector Quantization)類神經網路對膝部動脈血管拓樸形態之辨識,期能作為先期快速篩檢、輔助醫生診斷的工具,証實經過3組PAOD(peripheral arterial occlusive disease)病患和1組正常人影像,共計20張不同角度的影像的實例測試,辨識率高達85%,確實能有效診斷,並作為專家診斷系統並縮短病人等檢查報告時間,有助於整體醫療技術的改善及增進整體醫療的效率。 | zh_TW |
dc.description.abstract | The ways of angiography are divided into two kinds at present: the invasive type and the non invasive type. Because the magnetic resonance angiography (MRA) has advantages of the non invasive type, thus people can accept MRA more easily. Presently, to diagnoses for the initial stage triage of the blood vessel on clinic by MRA mostly. We to be allowed to see clearly that the shape of lower limb artery which like the dendrite and the blood vessel is thick from the trunk to the thin branch, also we can see the narrow embolism and the blocked place through MRA. This study is aiming at the image of artery of blood vessel by MRA assay, and is attempting to use two-dimensional structure of SOM and LVQ to make out topologies for the shape of artery of blood vessel. We expect that MRA could be useful tools for earlier on the quick triage and auxiliary diagnosis of doctors. By actual examples truly prove that patients after peripheral arterial occlusive disease (PAOD) treatment can diagnose effectively, shorten the time of patients waiting for reports and improve the whole efficiency of the medical treatment system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T16:27:17Z (GMT). No. of bitstreams: 1 ntu-94-P92921012-1.pdf: 4228619 bytes, checksum: 041b0a55a28a4c370de554b0d1b4587e (MD5) Previous issue date: 2005 | en |
dc.description.tableofcontents | 第一章 緒論
1.1 簡介 1 1.2 研究動機 2 1.3 研究目的 5 1.4 研究方法 7 1.5 論文架構 7 第二章 類神經網路原理 2.1 類神經網路架構 9 2.2 SOM類神經網路原理及架構 10 2.3 LVQ類神經網路原理及架構 14 2.4 SOM與LVQ結合模式 20 2.5 文獻回顧 22 第三章 濾波暨其他相關原理 3.1 小波濾波 23 3.2 高斯低通濾波 28 3.3 EXP Enhance 29 3.4 Otus method 30 第四章 系統架構設計 4.1 系統架構 33 4.2 濾波模組 34 4.3 SOM類神經網路 38 4.4 LVQ類神經網路 45 第五章 辨識系統實驗結果 5.1 測試影像暨測試方法 48 5.2 辨識結果 51 第六章 總結 6.1 結論 54 6.2 未來研究方向 55 參考文獻 56 | |
dc.language.iso | zh-TW | |
dc.title | 利用類神經網路於輔助膝部動脈血管之MRA影像辨識 | zh_TW |
dc.title | Identification of Knee Artery in MRA Images Using Neural Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 93-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 施庭芳 | |
dc.contributor.oralexamcommittee | 詹曉龍,林耀仁,林育德 | |
dc.subject.keyword | 磁振血管造影,自組映射,學習向量,周邊動脈阻塞, | zh_TW |
dc.subject.keyword | MRA,SOM,LVQ,PAOD, | en |
dc.relation.page | 58 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2005-07-15 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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