應用獨立成份分析法於超音波影像斑點的濾除
與主動輪廓切割

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廖振宏(Cheng-Hung Liao) 查詢紙本館藏

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電機工程學系

論文名稱

應用獨立成份分析法於超音波影像斑點的濾除與主動輪廓切割
(Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method )

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摘要(中)

中文摘要
超音波具有非侵入性、高解析度、即時性掃描、非放射性及使用方便等優點，因此常被使用於掃描生物體內的組織器官；因而成為臨床上最普遍使用的診斷工具。藉由超音波影像，有助於臨床醫師了解各種組織構造，並且可以發現壞損或病變的部份。
然而因為物理特性的關係，超音波在介質內傳遞時，無法避免地會有不同相位的現象發生，其造成的建設性干涉與破壞性干涉，使得影像中產生大量明暗相間的斑點(speckle)，這些斑點會影響醫師們的診斷，並降低影像的品質。
為了消除超音波影像中的斑點，本論文提出一種高階統計學的演算法-獨立成份分析法(Independent Component Analysis, ICA)，試圖利用獨立成份分析法濾除不規則分佈的超音波斑點雜訊；我們進一步針對影像中有意義的區塊，使用主動輪廓模型(Active Contour Model, ACM)來對異物進行影像切割。
本研究的實驗結果証實，獨立成份分析法確實可以有效的濾除超音波斑點雜訊，配合主動輪廓的影像切割，將來可幫助臨床醫師進行超音波影像掃瞄時，提供腫瘤組織偵測的辨識度。

摘要(英)

Abstract
Medical ultrasound systems have the advantages of non-invasion, high spatial resolution, real-time scanning, low-radiation dose, and convenience for use, so that ultrasound has been used as powerful diagnosis tool and widely applied to different clinical applications. Especially, the benefit of high spatial resolution of ultrasound image allows clinical physicians can easily identify tumors or malignant tissues based on their spatial morphology or tissue characteristics. Nevertheless, the presence of speckle pattern in ultrasound image, generated by mutual interference of many diffraction waves with different phases, can degrade the quality of ultrasound image, even results in poor recognizability of small tissues.
This dissertation aims to develop a speckle suppression technique based on independent component analysis (ICA), which is a multivariate high-order statistical method. Based on the independency between tissue image and ultrasound speckles, we decompose an ultrasound image into several sub-components. Only those sub-components which are not related to speckle noise are chosen for reconstructing speckle-suppressed ultrasound image. An Active Contour Model (ACM) is then applied in the following to segment the interested region from background image.
In our study, we found the utilization of ICA can effectively suppress the unwanted speckle noise. The ICA-based speckle-suppression method has been incorporated with active contour model to label important information on an ultrasound image. This combined method might provide an efficacious way to improve the recogniability and sensitivity of malignant tissues in ultrasound image scanning.

關鍵字(中)

★ 超音波斑點
★ 獨立成份分析法
★ 主動輪廓模型

關鍵字(英)

★ Ultrasound Speckle
★ Independent Component Analysis
★ Active Contour Model.

論文目次

目錄
中文摘要 I
英文摘要 II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章緒論 1
1-1 前言 1
1-2 研究動機 1
1-3 文獻回顧 2
1-4 研究方法 5
1-5 論文架構 5
第二章超音波影像原理 7
2-1 超音波簡介 7
2-2 超音波物理特性 7
2-2-1 聲速 7
2-2-2 反射與折射 8
2-2-3 散射 10
2-2-4 吸收 10
2-2-5 衰減 10
2-3 超音波裝置 11
2-3-1 換能器 11
2-3-2 探頭 12
2-3-3 A模式 13
2-3-4 B模式 15
第三章獨立成份分析法 17
3-1 獨立成份分析法簡介 17
3-2 獨立成份分析法理論 17
3-2-1 雞尾酒派對問題 17
3-2-2 ICA的模型 18
3-2-3 ICA的原理 18
3-3 資料前處理 19
3-3-1 資料置中 19
3-3-2 白化 20
3-4 使用負熵的固定點疊代法 20
3-4-1熵與負熵 20
3-4-2 固定點疊代法 23
3-5 獨立成份分析法流程 24
3-6 訊號分析舉例 26
第四章主動輪廓模型 28
4-1 主動輪廓模型簡介 28
4-2 演算法的實現 29
4-3 Chan-Vese模型 33
4-4等階集合函數 35
第五章實驗架構 39
5-1 模擬的超音波影像 39
5-2 實際的超音波影像 40
5-3 實驗步驟 42
第六章結果與討論 44
6-1 模擬的超音波影像 44
6-2 實際的超音波影像 55
6-3 結論與未來展望 62
參考文獻 63

參考文獻

參考文獻
[1]. K.K.Shung and G.A.Thieme, Ultrasound Scattering in Biological Tissues, CRC Press, Boca Raton, 1993.
[2]. 編/日本超音波醫學會, 譯/李嘉明李玉華, 新超音波醫學1-醫用超音波的基礎, 合記圖書出版社, 2003.
[3]. D.A.Christensen, Ultrasound Bioinstrumentation, Wiley, New York, 1988.
[4]. G.E.Trahey and S.W.Simith and O.T.von Ramm, Speckle pattern correlation with lateral aperture translation: Experimental results and implications for spatial compounding, IEEE Trans. Ultrason., Ferroelect. Freq. Contr., vol.Uffc3, no.3, pp.257-264, 1986.
[5]. G.E.Trahey and J.W.Allison and S.W.Smith and O.T.von Ramm, A quantitative approach to speckle reduction via frequency compounding, Ultrason.Imag., vol.8, no.3, pp.151-164, 1986.
[6]. Rafael C. Gonzales and Richard E. Woods, Digital Image Processing Second Edition, Prentice Hall, 2002.
[7]. T.Loupas and W.N.McDicken and P.L.Allan, An adaptive weighted median filter for speckle suppression in medical ultrasonic images, IEEE Trans. Circuits Syst., vol.36, no.1, pp.129-135, 1989.
[8]. Ping Yang and Otman A.Basir, Adaptive weighted median filter using local entropy for ultrasonic image de-noising, on Image and Signal Process. and Anal. in Proc. of 3rd Inc. Symp., vol.2, pp.799-803, 2003.
[9]. J.I.Koo and S.B.Park, Speckle reduction with edge preservation in medical ultrasonic images using a homogeneous region growing mean filter (HRGMF), Ultrason.Imag., vol.13, no.3, pp.211-237, 1991.
[10].Y.Chen and R.M.Yin and P.Flynn and S.Broschat, Aggressive region growing for speckle reduction in ultrasound images, Pattern Recognition Lett., vol.24, no.4-5, pp.677-691, 2003.
[11].A.Thakur and R.S.Anand, Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction, Digital Signal Process., vol.15, no.5, pp.455-465, 2005.
[12].F.Zhang and Y.M.Yoo and L.M.Koh and Y.kim, Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction, IEEE Trans. Med. Imag., vol.26, no.2, pp.200-211, 2007.
[13].Peter Fish, Physics and Instrumentation of Diagnostic Medical Ultrasound, John Wiley & Sons, 1992.
[14].著/島川正憲, 譯/賴耿陽, 超音波工學理論實務, 復漢出版社, 1998.
[15].A.Hyvarinen, Fast and robust fixed-point algorithms for Independent Component Analysis, IEEE Transactions on Neural Networks, vol.10, pp.626-634, 1999.
[16].P.Comon, Independent Component Analysis, A new concept? , Signal Processing, Elsevier, vol.36, pp.287–314, 1994.
[17].A.Ravindran and K.M.Ragsdell and G.V.Reklaitis, Engineering Optimization Second Edition, John Wiley & Sons, 2006.
[18].Aapo Hyvarinen and Juha Karhunen and Erkki Oja, Independent Component Analysis, John Wiley & Sons, 2001.
[19].Kullback and Solomon, Information Theory and Statistics, Dover Publications Gloucester, 1978.
[20].Larson Edwards, Elementary Linear Algebra Fourth Edition, Houghton Mifflin, 2000.
[21].M.Kass and A.Witkin and D.Terzopoulos, Snakes: Active Contour Models, International Journal of Computer Vision, vol.1, no.4, pp.321-331, 1988.
[22].Tony F. Chan and Luminita A. Vese, Active Contours Without Edges, IEEE Trans. Image Processing, vol.10, pp.266-277, 2001.
[23].Stanley Osher and Ronald Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Springer-Verlag New York, 2003.
[24].J.A.Jensen, Field: A program for simulating ultrasound systems, on biomedical imaging published in Medical & Biological Engineering & Computing, 10th Nordic-Baltic conference vol.34, pp. 351-353, 1996.
[25].http://server.oersted.dtu.dk/personal/jaj/field/
[26].http://www.terason.com/
[27].Chris Ding and Xiao Feng He, K-means clustering via Principal Component Analysis, Proc. of Int'l Conf. Machine Learning, pp 225-232, 2004.

指導教授

李柏磊(Po-Lei Lee)

審核日期

2009-2-6

推文