請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23880
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曹建和 | |
dc.contributor.author | Tsung-Han Tsai | en |
dc.contributor.author | 蔡宗翰 | zh_TW |
dc.date.accessioned | 2021-06-08T05:12:01Z | - |
dc.date.copyright | 2006-07-26 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-07-21 | |
dc.identifier.citation | [1] Bakker, J., Olree, M., Kaatee, R., de Lange, E.E., Moons, K.G.M, Beutler, J.J., Beek, R.J.A., “Accuracy and repeatability of US compared with that of MR imaging Radiology”, 211, 623-628, 1999.
[2] N. Friedland, D. Adam, “Automatic Ventricular Cavity Boundary Detection from sequential Ultrasound Images Using Simulated Annealing”, IEEE Trans on Medical imaging, Vol.8, NO.4,1989. [3] Richard O. Duda, Peter E. Hart, David G. Stork, “Pattern Classification”, 2001. [4] Whittle, P., “On stationary processes in the plane”, Biometrika, vol.41 ,434-449, 1954. [5] Abend, K., T.J. Harley, L.N. Kanal, “Classification of binary random pattern”, IEEE Trans, Information Theory, Vol. IT-11, 538-544, 1965. [6] Dobrushin, R.L., ”The description of random field by means of conditional probabilities and conditions of its regularity”, Theory Prob. Appl., Vol.13, 197-224, 1968. [7] Besag, J., “Spatial interaction and the statistical analysis of lattice systems”, J.R.Statist. Soc. B., Vol.36, 192-236, 1974. [8] Moran, P.A.P, “A Gaussian Markovian process on a square lattice”, J.Appl. Prob., Vol.10, 54-62, 1973. [9] Woods, J.W., “Two-dimensional discrete Markovian fields”, IEEE Trans, Information Theory, Vol.18, 232-240, 1972. [10] Geman, S. and D. Geman, “Stochastic relaxtion, Gibbs distribution, and the Bayesian restoration of images”, IEEE Trans, Pattern Analysis and Machine Intelligence, Vol.6 ,NO.6 , 721-741, 1984. [11] Gerhard Winker, “Image Analysis, Random Fields and Markov Chain Monte Carlo Methods”, 2002. [12] Chee Sun Won , Robert M. Gray, “Stochastic Image Processing” ,2004. [13] Geir Storvilk, “A Bayesian Approach to Dynamic Contours through Stochastic Sampling and Simulated Annealing”, IEEE Trans on Pattern Analysis and Machine Intelligence, Vol.16, NO.10, 1994. [14] Christine Haas, Helmut Ermert, “Segmentation of 3D intravascular ultrasonic images based on a random field model”, Ultrasound in Med. & Biol., Vol.26, NO.2, 297-306, 2000. [15] Marcos Martin-Fernandez, Carlos Alberola-Lopez, “An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours”, Medical Image Analysis, 2004. [16] M. Martin, R. San Jose, C. Alberola, “Maximum likelihood contour estimation using beta-statistics in ultrasound images”, Proc. IEEE ICASSP, Pula, Croatia, 2001. [17] Saeed Ghahramani, “Fundamentals of Probability”, PRENTICE HALL, 1996. [18] Marcos Martin, Carlos Alberola, “A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields”, MICCAI, LNCS 2489, 397-404, 2002. [19] Paul B. Chou & Christpoher M. Brown, “The Theory and Practice of Bayesian Image Labeling”, International Journal of Computer Vision, Vol.4, 185-210, 1990. [20] Wei Yao, Jianming Tian, “Star algorithm: Detecting the ultrasonic endocardial boundary automatically”, Ultrasound in Med. & Biol., Vol.30, NO.7, 943-951, 2004. [21] Jacob G, Noble JA, Behrenbruch C, etc, “A shape-space-based approach to tracking myocardial borders and quantifying regional left ventricular function applied in echocardiography”, IEEE Ttrans Med Imaging, Vol.21, 226-238, 2002. [22] Richard C. Dubes & Anil K. Jain, “Random field models in image analysis”, Journal of Applied Statistics, Vol.16, NO.2, 1989. [23] Max Mignotte, Jean Meunier, Jean-Claude Tardif, “Endocardial Boundary Estimation and Tracking in Echocardiographic Images using Deformable Templates and Markov Random Fields”, Pattern Analysis & Applications, Vol.4, 256-271, 2001. [24] Gonzalez Woods, “Digital Image Processing”, 2/e. [25] 張立欣, “使用超音波影像之腎結石影像偵測與辨識”, 國立台灣大學電機工程研究所碩士論文, 6/2005. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23880 | - |
dc.description.abstract | 腎臟的體積在臨床診斷上是個很重要的參數,無論是對成人、新生兒、或是三個月後的胎兒都是如此,以成人為例,醫師會藉由腎臟的長度跟體積來評估或是追蹤病人是否患有尿道感染、腎血管狹窄等疾病。至於新生兒或是三個月後的胎兒,醫師會藉由異常大的腎臟體積來判斷是否患有新生兒腎盂積水等疾病,所以腎臟邊界的偵測對於醫師判讀病理來說,是很重要的一環。而至今的文獻,對於腎臟超音波影像邊界偵測的問題,仍然沒有提出一套完整而且有效的解決方法出來。本研究則是以星狀演算法為主軸,並搭配馬可夫隨機場的理論基礎,來偵測腎臟超音波影像的邊界,目的是希望能結合星狀演算法與馬可夫隨機場的優點,發展出一套近乎全自動化、且運作快速的演算法,以達到電腦輔助診斷的目的。 | zh_TW |
dc.description.abstract | Renal volume is an important parameter in clinical settings both for the adult, newborns and fetuses. About the former, evaluation and follow-up of patients with urinary tract infections, renal vessels stenosis and others are done in terms of both the length and the volume within the organ. About newborns and fetuses, the neonatal hydroneohrosis is detected by means of abnormal large volumes enclosed by the organ. Therefore, the boundary detection problem of kidney is important for the doctors to diagnose pathology. But Solutions proposed so far in the literature are very application-driven so they do not constitute a complete and valid method when applied to the boundary detection problem of ultrasonic kidney images. This research is based on star algorithm, and matches the rationale of markov random fields to detect the boundary of ultrasonic kidney images. We hope to combine the advantages of star algorithm and markov random fields, and develop an algorithm with near automation and high speed for the purpose of the computer aided diagnosis. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T05:12:01Z (GMT). No. of bitstreams: 1 ntu-95-R93921130-1.pdf: 2484972 bytes, checksum: 489dd7e19b086aed66df0ea66f1870c5 (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | 第一章 緒論
1.1 前言。。。。。。。。。。。。。。。。。。。。。。。。。1 1.2 超音波原理簡介。。。。。。。。。。。。。。。。。。。。2 1.3 研究動機與目標。。。。。。。。。。。。。。。。。。。。4 1.4 論文架構。。。。。。。。。。。。。。。。。。。。。。。6 第二章 基本原理 2.1 星狀演算法。。。。。。。。。。。。。。。。。。。。。。7 2.2 貝氏決定理論。。。。。。。。。。。。。。。。。。。。。8 2.3 最大概似估計法。。。。。。。。。。。。。。。。。。。。11 2.4 馬可夫隨機場。。。。。。。。。。。。。。。。。。。。。15 2.4.1 馬可夫隨機場簡介。。。。。。。。。。。。。。。。。。17 2.4.2 吉伯斯隨機場簡介。。。。。。。。。。。。。。。。。。19 2.4.3 最大事後機率的估測。。。。。。。。。。。。。。。。。22 第三章 演算法介紹 3.1 演算法架構。。。。。。。。。。。。。。。。。。。。。。25 3.2 演算法流程圖。。。。。。。。。。。。。。。。。。。。。27 3.3 銅幣影像的中心點偵測。。。。。。。。。。。。。。。。。28 3.4 腎臟影像的中心點偵測。。。。。。。。。。。。。。。。。35 3.5 變形區域與事後取樣。。。。。。。。。。。。。。。。。。44 第四章 效能評估 4.1 效能評估定義。。。。。。。。。。。。。。。。。。。。。50 4.2 實驗結果。。。。。。。。。。。。。。。。。。。。。。。50 4.3 效能評估結果。。。。。。。。。。。。。。。。。。。。。57 第五章 結論與展望。。。。。。。。。。。。。。。。。。。58 | |
dc.language.iso | zh-TW | |
dc.title | 超音波腎臟影像的邊界偵測演算法開發 | zh_TW |
dc.title | A Boundary Detection Algorithm for Ultrasonic Kidney Images | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 郭益源,鄭建華 | |
dc.subject.keyword | 超音波影像,邊界偵測,星狀演算法, | zh_TW |
dc.subject.keyword | ultrasonic images,boundary detection,star algorithm, | en |
dc.relation.page | 61 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2006-07-22 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-95-1.pdf 目前未授權公開取用 | 2.43 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。