以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：21

、訪客IP：3.141.166.242

姓名	陳耀添(Yao-Tien Chen)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	以統計推論為基礎的醫學影像壓縮與分割方法 (Medical Image Compression and Segmentation based on Statistical Inferences)
相關論文	★ 適用於大面積及場景轉換的視訊錯誤隱藏法 ★ 虛擬觸覺系統中的力回饋修正與展現 ★ 多頻譜衛星影像融合與紅外線影像合成 ★ 腹腔鏡膽囊切除手術模擬系統 ★ 飛行模擬系統中的動態載入式多重解析度地形模塑 ★ 以凌波為基礎的多重解析度地形模塑與貼圖 ★ 多重解析度光流分析與深度計算 ★ 體積守恆的變形模塑應用於腹腔鏡手術模擬 ★ 互動式多重解析度模型編輯技術 ★ 以小波轉換為基礎的多重解析度邊線追蹤技術(Wavelet-based multiresolution edge tracking for edge detection) ★ 基於二次式誤差及屬性準則的多重解析度模塑 ★ 以整數小波轉換及灰色理論為基礎的漸進式影像壓縮 ★ 建立在動態載入多重解析度地形模塑的戰術模擬 ★ 以多階分割的空間關係做人臉偵測與特徵擷取 ★ 以小波轉換為基礎的影像浮水印與壓縮 ★ 外觀守恆及視點相關的多重解析度模塑
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	近年來隨著醫學影像設備的精進及普及，醫學影像數量的累積速度愈來愈快;對於醫學影像的傳輸、儲存、診斷分析、和治療支援需求日益增多，其中醫學影像壓縮和醫學影像分割技術是不可或缺的技術。在本研究中，我們基於統計推論 (statistical inference) 的原理，提出二個醫學影像壓縮方法及一個醫學影像分割方法。 在第一個醫學影像壓縮中，我們提出一個以小波 (wavelet) 為基礎具有動態預測 (adaptive prediction) 功能的無損影像壓縮方法。這個方法包含了三個主要的步驟: (i) 首先我們分析小波係數的相關性 (correlation analysis) ，透過這個分析我們挑選出一個適合用來壓縮醫學影像的lifting整數小波基底函數，並使用這個小波基底函數做影像的小波分解; (ii) 為了要決定那些小波係數應該被放在預測方程式的模型中 (那些小波係數要被當成是預測方程式中的預測變數)，才會得到最佳的預測，我們將HLi, LHi, 和 HHi (i = 1, 2, 3) 次頻帶 (subband) 上的小波係數以統計檢定 (statistical F Test) 進行檢定，經由統計檢定，我們得到三個分別對應於三個不同性質小波樹 (wavelet tree) 的預測方程式，即所謂的動態預測方法; (iii) 透過動態預測，我們得到各小波係數的預測值 (predicted value)，並由預測值與真正值 (original value) 相減計算出預測差值 (prediction error); 再以動態算術編碼法 (adaptive arithmetic encoder) 編碼預測差值。取代使用固定數目的預測變數 (predictor) 在固定的位置上做預測，我們提出以動態預測方法來克服預測變數 (predictor variables) 間的共線性問題 (multicollinearity)。我們所提的方法整合了相關性分析選擇小波基底函數，且利用動態預測挑選預測方程式中的預測變數，所以我們的方法能夠完成高準確的預測。我們也比較了其它著名的壓縮方法;在電腦斷層掃描影像 (CT)、核磁共振影像 (MRI)、和超音波影像 (ultrasound images) 的無損壓縮上，我們所提的方法能夠提供較高的壓縮率。 更進一步，為了提供多樣性region-of-interest (ROI) 的需求，包含了漸近式傳輸、多邊形ROI、多個ROI需求，我們提出了另一個具有多邊形 ROI之漸近式影像壓縮方法。首先，我們利用所提的分割及合併 (split and mergence) 演算法將凹多邊形ROI 分割成多個凸多邊形ROI，第二，我們利用列掃描方式 (row-order scan) 和動態算術編碼法來編碼這多個凸多邊形ROI裡的像點。第三，我們將影像中ROI區域內的像點以零值取代，並用lifting integer wavelet transform 來分解整個影像，第四，我們將此小波分解後的影像利用所提的第一個醫學影像壓縮方法來處理，此步驟執行完後會得到影像的預測差值;最後，我們再利用動態算術編碼法編碼預測差值。 為了要協助醫學影像上的分析和判讀，使得醫生們能夠更準確地辨識出相關的病因或症狀，我們提出一個以貝氏風險 (Bayesian risk) 為基礎的等階集合 (level set) 醫學影像分割方法。首先，影像分割問題被看作成一個影像點的分類問題。然後，透過像點分類時所造成的誤分類損失(the loss of misclassification)，我們定義相對應的貝氏風險 (Bayesian risk)。經由極小化此誤分類的風險，我們推論出能夠找到目標邊緣的等階集合演化泛函式 (level set evolution functional)。接下來，為了避免演化曲線 (propagating curves) 產生太多不規則形狀的區段和產生過多的小區域，影像的曲率 (curvature) 和梯度 (gradient) 資訊也被整合到此泛函式裡。最後，Euler-Lagrange 公式被用來推導等階集合演化方程式。比較其它等階集合方法，我們的方法是依賴像點分類最佳化決定所推導出的;所以我們的方法在理論和實務上都有可靠的信賴度。實驗也顯示這個方法能夠正確地擷取出複雜的目標物形狀，且對於多種不同的影像;例如，高雜訊低對比影像、各種類型雜訊影像、各種類型模糊影像、各類型醫學影像等，我們都能得到準確的分割結果。更進一步，我們所提的方法可以很容易的被延伸到多區域 (multiphase) 分割問題上。 在這篇論文中，統計推論是我們方法的主要基礎，在第一個方法中，由於使用相關性分析和統計檢定來完成壓縮;所以能完成較高的壓縮效果，在第二個方法中，我們將壓縮功能加以延伸，使得原方法能夠進一步地提供多樣性ROI的需求，在第三個方法中，統計決策理論 (statistical decision theories) 被整合到貝式等階集合分割方法 (Bayesian level set method) 中推導;因此所提的分割方法適合用在複雜的醫學影像分割上。
摘要(英)	As image acquisition devices are rapidly evolving and extreme amount of medical images are daily produced, the needs for medical image transmission, storage, diagnostic analysis, and therapeutic support are ever-increasing. Among the technologies to achieve the needs mentioned above, medical image compression and segmentation are two of the indispensable technologies. In this dissertation, we propose the medical image compression and segmentation methods based on the principles of statistical inference. To achieve the compression of medical images, a lossless wavelet-based image compression method with adaptive prediction (WCAP) is proposed. The proposed method consists of three steps: (i) the correlations between wavelet coefficients are analyzed to identify a proper wavelet basis function; (ii) predictor variables are statistically test to determine which relative wavelet coefficients should be included in the prediction model; (iii) prediction differences are encoded by an adaptive arithmetic encoder. Instead of relying on a fixed number of predictors on fixed locations, we propose the adaptive prediction approach to overcome the multicollinearity problem of predictor variables. The proposed approach integrating correlation analysis for selecting wavelet basis function with predictor variable selection is fully achieving high accuracy of prediction. Comparing with several state-of-the-art methods, the proposed approach achieves a higher compression ratio on computed tomography (CT), magnetic resonance (MRI), and ultrasound images. Moreover, to provide the variety requirements of region of interest (ROI) coding containing progressive transmission, polygon-shaped ROI, and multiple ROIs, we further propose another progressive lossy-to-lossless compression technique. Firstly, split and mergence algorithms were proposed to separate concave ROIs into smaller convex ROIs. Secondly, row-order scan and an adaptive arithmetic coding were used to encode the pixels in ROIs. Thirdly, a lifting integer wavelet transform was used to decompose the original image in which the pixels in the ROIs had been replaced by zeros. Fourthly, the WCAP method was used to obtain predicted coefficients for difference encoding. Finally, the adaptive arithmetic coding was also adopted to encode the differences between the original and corresponding predicted coefficients. The proposed method only needs less shape information to record the shape of ROI and provides a lossy-to-lossless coding function; thus the approach is suitable for achieving the variety of ROI requirements including polygon-shaped ROI and multiple ROIs. Experimental results show that the proposed lossy-to-lossless coding with ROI function reduces bit rate as comparing with the MAXSHIFT method in JPEG2000. To assist doctors to analyze and explore the medical images, a level set method based on the Bayesian risk is proposed, so that doctors can make better diagnosis and accurately examine disease symptoms. At first, the image segmentation is formulated as a classification of pixels. Then the Bayesian risk is formed by the losses of pixel classification. Through minimizing the risk of misclassification, the level set evolution functional is deduced for finding the boundaries of targets. To prevent the propagating curves from generating excessively irregular shapes and lots of small regions, curvature and gradient of edges in the image are integrated into the functional. Finally, the Euler-Lagrange approach is used to find the iterative level set equation from the derived functional. Comparing with other level-set methods, the proposed approach relies on the optimum decision of pixel classification; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach can accurately extract the complicated shape of targets and is robust for various types of images including high-noisy and low-contrast images. Moreover, the algorithm is extendable for multiphase segmentation. In this dissertation, we proposed medical image compression and segmentation methods which are mainly derived from the statistical inferences. In the first technique, we adopt correlation analysis to identify a proper wavelet basis function, and statistical F test to adaptively select predictor variable; thus the proposed WCAP approach can achieve high compression ratio on various medical images. In the second technique, we further extended the lossless compression technique to provide the variety of ROI requirements. In the third technique, the statistical decision theories are integrated into the derivation of Bayesian level set method; thus the proposed segmentation method is suitable for complicated medical image segmentation.
關鍵字(中)	★ 貝氏風險 ★ 漸近式傳輸 ★ 動態預測 ★ 統計推論 ★ 影像分割 ★ 影像壓縮 ★ 醫學影像 ★ 等階集合法	關鍵字(英)	★ Bayesian risk ★ ROI coding ★ progressive transmission ★ adaptive prediction ★ statistical inference ★ image segmentation ★ image compression ★ medical image ★ level set method
論文目次	中文摘要 i Abstract iii 誌謝 vi Contents vii List of Figures x List of Tables xiii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Overview of the study 2 1.2.1 Wavelet-based medical image compression with adaptive prediction 2 1.2.2 Wavelet-based image compression with polygon-shaped region of interest 3 1.2.3 A level set method based on the Bayesian risk for medical image segmentation 3 1.3 Organization of dissertation 4 Chapter 2 Related Works 5 2.1 Image compression methods 5 2.2 ROI coding methods 11 2.3 Image segmentation methods 14 Chapter 3 Wavelet-based Medical Image Compression with Adaptive Prediction 24 3.1 The procedure of the WCAP method 24 3.2 Lifting integer wavelet transforms 25 3.3 The selection wavelet basis function 30 3.4 The selection of predictor variables 33 3.5 Experiments 36 Chapter 4 Wavelet-based Image Compression with Polygon-Shaped Region of Interest 44 4.1 The definition of ROIs 44 4.2 The split and mergence algorithms 46 4.3 Experiments 49 Chapter 5 A Level Set Method Based on The Bayesian Risk for Medical Image Segmentation 58 5.1 The statistical decision theories 58 5.1.1 Conditional average risk 58 5.1.2 Bayesian risk 60 5.2 Level set models 61 5.2.1 Two-phase level set models 61 5.2.2 Multiphase level set models 64 5.3 Experiments 66 Chapter 6 Conclusions and Future Works 84 6.1 Conclusions 84 6.2 Future works 88 References 90 Appendix A (Use the Euler-Lagrange equation to find the iterative level-set equation) 99 Appendix B (Convert the textured image into the graylevel image) 103
參考文獻	[1] Armstrong, P. and M. L. Wastie, Diagnostic Imaging, Blackwell Scientific Publications, London, UK, 1989. [2] Aujol, J., G. Aubert, and L. Blanc-Feraud, "Wavelet-based level set evolution for classification of textured images," IEEE Trans. Image Processing, vol.12, no.12, pp.1634-1641, Dec. 2003. [3] Aydin, T., Y. Yemez, E. Anarim, and B. Sankur, "Multidirectional and multiscale edge detection via M-band wavelet transform," IEEE Trans. Image Processing, vol.5, no.9, pp.1370-1377, Sep. 1996. [4] Berg, M. de, M. van Kreveld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications. Springer, Berlin, 2000. [5] Bow, S.-T., Pattern Recognition and Image Preprocessing, Marcel Dekker Inc., New York, 2002. [6] Brechtken-Manderscheid, U., Introduction to the Calculus of Variations, Chapman & Hall, London, UK, 1991. [7] Bryan, G. J., Diagnostic Radiography: A Concise Practical Manual, Churchill Livingstone Inc., New York, 1987. [8] Buccigrossi, R. W. and E. P. Simoncelli, "Image compression via joint statistical characterization in the wavelet domain," IEEE Trans. Image Processing, vol.8, no.12, pp.1688-1701, Dec. 1999. [9] Calderbank, A. R., I. Daubechies, W. Sweldens, and B. Yeo, "Wavelet transforms that map integers to integers," Applied and Computational Harmonic Analysis, vol.5, no.3, pp.332-362, July 1998. [10] Casella, G. and R. L. Berger, Statistical Inference, Calif Wadsworth & Brooks/Cole, CA, 1990. [11] Chan, T. F., B. Y. Sandberg, and L. A. Vese, "Active contours without edges for vector-valued images," Journal of Visual Communication and Image Representation, vol.11, no.2, pp.130-141, June 2000. [12] Chan, T. F. and L. A. Vese, "Active contours without edges," IEEE Trans. Image Processing, vol.10, no.2, pp.266-277, Feb. 2001. [13] Chan, T. F. and L. A. Vese, Image Segmentation Using Level Sets and The Piecewise-constant Mumford-Shah Model, UCLA CAM Report, Dept. of Mathematics, Univ. of California, Los Angeles, 2000. [14] Chan, T. F. and L. A. Vese, "A level set algorithm for minimizing the Mumford-Shah functional in image processing," in Proc. IEEE Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, BC, Canada, 2001, pp.161-168. [15] Chenoune, Y., E. Delechelle, E. Petit, T. Goissen, J. Garot, and A. Rahmouni, "Segmentation of cardiac cine-MR images and myocardial deformation assessment using level set methods," Computerized Medical Imaging and Graphics, vol.29, no.8, pp.607-616, Dec. 2005. [16] Christopoulos, C., A. Skodras, and T. Ebrahimi, "JPEG2000 still image coding system: an overview," IEEE Trans. Consumer Electronics, vol.46, no.4, pp.1103-1127, Nov. 2000. [17] Christopoulos, C., J. Askelof, and M. Larsson, "Efficient methods for encoding regions of interest in the upcoming JPEG2000 still image coding standard," IEEE Signal Processing Letters, vol.7, no.9, pp.247-249, Sep. 2000. [18] Davatzikos, C. and J. L. Prince, "An active contour model for mapping the cortex," IEEE Trans. Medical Imaging, vol.14, no.1, pp.65-80, Mar. 1995. [19] Deever, A. T. and S. S. Hemami, "Lossless image Compression with projection-based and adaptive reversible integer wavelet transforms," IEEE Trans. Image Processing, vol.12, no.5, pp.489-499, May 2003. [20] Dillen, G., B. Georis, J. Legat, and O. Cantineau, "Combined line-based architecture for the 5-3 and 9-7 wavelet transform of JPEG2000," IEEE Trans. Circuits and Systems for Video Technology, vol.13, no.9, pp.944-950, Sep. 2003. [21] Dilmaghani, R. S., A. Ahmadian, M. Ghavami, and A. H. Aghvami, "Progressive medical image transmission and compression," IEEE Signal Processing Letters, vol.11, no.10, pp.806-809, Oct. 2004. [22] Drapaca, C. S., V. Cardenas, and C. Studholme, "Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets," Computer Vision and Image Understanding, vol.100 no.3, pp.312-329, Dec. 2005. [23] Fieguth, P. and D. Terzopoulos, "Color-based tracking of heads and other mobile objects at video frame rates," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Juan, WA, Jun.17-19, 1997, pp.21-27. [24] Freund, R. J. and W. J. Wilson, Regression Analysis: Statistical Modeling of A Response Variable, Academic Press, San Diego, 1998. [25] Fukuma, S., T. Tanaka, and M. Nawate, "Switching wavelet transform for ROI image coding," IEICE Trans. Fund. Electron. Commun. Comput. Sci., vol.E88, no.7, pp.1995-2005, July 2005. [26] Goldberg, R., R. Kimmel, E. Rivlin, and M. Rudzsky, "Cortex segmentation: A fast variational geometric approach," IEEE Trans. Medical Imaging, vol.21, no.12, pp.1544-1551, Dec. 2002. [27] Gruter, R., O. Egger, J.-M. Vesin, and M. Kunt, "Rank-order polynomial subband decomposition for medical image compression," IEEE Trans. Medical Imaging, vol.19, no.10, pp.1044-1052, Oct. 2000. [28] Hong, L., Y. Wan, and A. Jain, "Fingerprint image enhancement: algorithm and performance evaluation," IEEE Trans. Pattern Anal. Machine Intell., vol.20, no.8, pp.777-789, Aug. 1998. [29] Jeon, M., M. Alexander, W. Pedrycz, and N. Pizzi, "Unsupervised hierarchical image segmentation with level set and additive operator splitting," Pattern Recognition Letters, vol.26, no.10, pp.1461-1469, July 2005. [30] Kass, M., A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol.1, no.4, pp.321-331, 1987. [31] Kassim, A. A., P. Yan, W. S. Lee, and K. Sengupta, "Motion compensated lossy-to-lossless compression of 4-D medical images using integer wavelet transforms," IEEE Trans. Information Technology in Biomedicine, vol.9, no.1, pp.132-138, Mar. 2005. [32] Kaur, L., R. C. Chauhan, and S. C. Saxena, "Adaptive compression of medical ultrasound images," IEE Proc. Vision, Image, and Signal Processing, vol.153, no.2, pp.185-190, Apr. 2006. [33] Kim, B.-J., Z. Xiong, and W. A. Pearlman, "Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)," IEEE trans. Circuits and Systems for Video Technology, vol.10, no.8, pp.1374-1387, Dec. 2000. [34] Krishnan, K., M. W. Marcellin, A. Bilgin, and M. Nadar, "Prioritization of compressed data by tissue type using JPEG2000," Medical Imaging 2005 - PACS and Imaging Informatics, San Diego, CA, United States, Feb.15-17, 2005, vol.5748, pp.181-189. [35] Lee, S.-H. and J. K. Seo, "Level set-based bimodal segmentation with stationary global minimum," IEEE Trans. Image Processing, vol.15, no.9, pp.2843-2852, Sep. 2006. [36] Li, S. and W. Li, "Shape-adaptive discrete wavelet transforms for arbitrarily shaped visual object coding," IEEE Trans. Circuits and Systems for Video Technology, vol.10, no.5, pp.725-743, Aug. 2000. [37] Li, X. and M. T. Orchard, "Edge-directed prediction for lossless compression of natural images," IEEE Trans. Image Processing, vol.10, no.6, pp. 813-817, June 2001. [38] Li, X. and M. T. Orchard, "New edge-directed interpolation," IEEE Trans. Image Processing, vol.10, no.10, pp.1521-1527, Oct. 2001. [39] Liu, L. and G. Fan, "A new JPEG2000 region-of-interest image coding method: partial significant bitplanes shift," IEEE Signal Processing Letters, vol.10, no.2, pp.35-38, Feb. 2003. [40] Liu, Z., Z. Xiong, Q. Wu, Y. Wang, and K. Castleman, "Cascaded differential and wavelet compression of chromosome images," IEEE Trans. on Biomedical Engineering, vol.49, no.4, pp.372-383, Apr. 2002. [41] Malladi, R., J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: a level set approach," IEEE Trans. Pattern Anal. Mach. Intell., vol.17, no.2, pp.158-175, Feb. 1995. [42] Martin, P., P. Refregier, F. Goudail, and F. Guerault, "Influence of the noise model on level set active contour segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol.26, no.6, pp.799-803, June 2004. [43] McInerney, T. and D. Terzopoulos, "Dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis," Computerized Medical Imaging and Graphics, vol.19, no.1, pp.69-83, 1995. [44] Mendenhall, W. and T. Sincich, Statistics for Engineering and The Sciences, Prentice-Hall, New Jersey, 1994. [45] Moffat, A., "Linear time adaptive arithmetic coding," IEEE Trans. Information Theory, vol.36, no.2, pp.401-406, Mar. 1990. [46] Mukherjee, D. P., N. Ray, and S. T. Acton, "Level set analysis for leukocyte detection and tracking," IEEE Trans. Image Processing, vol.13, no.4, pp.562-572, Apr. 2004. [47] Mumford, D. and J. Shah, "Optimal approximation by piecewise smooth functions and associated variational problems," Commun. Pure Appl. Math, vol.42, pp.577-685, 1989. [48] Nummiaro, K., E. Koller-Meier, and L. V. Gool, "An adaptive color-based particle filter," Image and Vision Computing, vol.21, no.1, pp.99-110, Jan. 2003. [49] Oliver, J. and M. P. Malumbres, "Low-complexity multiresolution image compression using wavelet lower trees," IEEE Trans. Circuits and Systems for Video Technology, vol.16, no.11, pp.1437-1444, Nov. 2006. [50] Osher, S. and J. A. Sethian, "Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations," Journal of Computational Physics, vol.79, no.1, pp.12-49, 1988. [51] Osher, S. and R. Fedkiw, "Level set methods: An overview and some recent results," Journal of Computational Physics, vol.169, pp.475-502, 2001. [52] Osher, S. and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Springer-Verlag, New York, 2003. [53] Paragios, N. and R. Deriche, "Geodesic active regions and level set methods for supervised texture segmentation," International Journal of Computer Vision, vol.46, no.3, pp.223-247, Mar. 2002. [54] Park, K.-H. and H. W. Park, "Region-of-interest coding based on set partitioning in hierarchical trees," IEEE trans. Circuits and Systems for Video Technology, vol.12, no.2, pp.106-113, Feb. 2002. [55] Pianykh, O. S. and J. M. Tyler, "Compression ratio boundaries for predictive signal compression," IEEE Trans. Image Processing, vol.10, no.2, pp.323-326, Feb. 2001. [56] Pianykh, O. S., J. M. Tyler, and R. Sharman, "Nearly-lossless autoregressive image compression," Pattern Recognition Letters, vol.20, no.2, pp.221-228, Feb. 1999. [57] Przelaskowski, A., "Lossless encoding of medical images: Hybrid modification of statistical modeling-based conception," Journal of Electronic Imaging, vol.10, no.4, pp.966-976, Oct. 2001. [58] Qi, X. and J. M. Tyler, "A progressive transmission capable diagnostically lossless compression scheme for 3D medical image sets," Information Sciences, vol.175, pp.217-243, 2005. [59] Robinson, J. A., "Adaptive prediction trees for image compression," IEEE Trans. Image Processing, vol.15, no.8, pp.2131-2145, Aug. 2006. [60] Romberg, J. K., H. Choi, and R. G. Baraniuk, “Bayesian tree-structured image modeling using wavelet-domain hidden Markov models,” IEEE Trans. Image Processing, vol.10, no.7, pp.1056-1068, July 2001. [61] Ryabko, B. and J. Rissanen, "Fast adaptive arithmetic code for large alphabet sources with asymmetrical distributions," IEEE Communications Letters, vol.7, no.1, pp.33-35, Jan. 2003. [62] Said, A. and W. A. Pearlman, "A new fast and efficient image codec based on set partitioning in hierarchical trees," IEEE Trans. Circuits and Systems for Video Technology, vol.6, pp.243-250, June 1996. [63] Shapiro, J. M., "Embedded image coding using zerotrees of wavelet coefficients," IEEE Trans. Signal Processing, vol.41, no.12, pp.3445-3462, Dec. 1993. [64] Sheng, F., A. Bilgin, P. J. Sementilli, and M. W. Marcellin, "Lossy and lossless image compression using reversible integer wavelet transforms," in Proc. Int. Conf. Image Processing, Chicago, IL, United States, Oct.4-7, 1998, vol.3, pp.876-880. [65] Siddiqi, K., Y. B. Lauziere, A. Tannenbaum, and S. W. Zucker, "Area and length minimizing flows for shape segmentation," IEEE Trans. Image Processing, vol.7, no.3, pp.433-443, Mar. 1998. [66] Strickland, R. N., Image-Processing Techniques Tumor Detection, Marcel Dekker Inc., New York, 2002. [67] Suri, J. S., K. Liu, S. Singh, S. N. Laxminarayan, X. Zeng, and L. Reden, "Shape recovery algorithms using level sets in 2-D/3-D medical imagery: A state-of-the-art review," IEEE Trans. Information Technology in Biomedicine, vol.6, no.1, pp.8-28, Mar. 2002. [68] Sweldens, W., "The lifting scheme: A custom-design construction of biorthogonal wavelets," Journal of Appl. and Comput. Harmonic Analysis, vol.3, no.2, pp.186-200, Apr. 1996. [69] Tai, S.-C., Y.-Y. Chen, and W.-C. Yan, "New high-fidelity medical image compression based on modified set partitioning in hierarchical trees," Optical Engineering, vol.42, no.7, pp.1956-1963, July 2003. [70] Tasdoken, S. and A. Cuhadar, "ROI coding with integer wavelet transforms and unbalanced spatial orientation trees," in Proc. of the 25th Annual Int. Conf. of the IEEE Eng. in Medicine and Biology Society, Cancun, Mexico, vol.1, Sep.17-21, 2003, pp.841-844. [71] Terzopoulos, D. and K. Fleischer, "Modeling inelastic deformation: viscoelasticity, plasticity, fracture," Computer Graphics ACM, vol.22, no.4, pp.269-278, Aug. 1988. [72] Tsai, A., A. Yezzi Jr., W. Wells, C. Tempany, D. Tucker, A. Fan, W. E. Grimson, and A. Willsky, "A shape-based approach to the segmentation of medical imagery using level sets," IEEE Trans. Medical Imaging, vol.22, no.2, pp.137-154, Feb. 2003. [73] Tseng, D.-C. and M.-Y. Shih, "Wavelet-based image denoising using contextual hidden Markov tree model," Journal of Electronic Imaging, vol.14, no.3, 033005 (12 pp.), July 2005. [74] Wang, Z. and A. C. Bovik, "Bitplane-by-Bitplane shift (BbBShift) - a suggestion for JPEG2000 region of interest image coding," IEEE Signal Processing Letters, vol.9, no.5, pp.160-162, May 2002. [75] Weickert, J., B. M. ter Haar Romeny, and M. A. Viergever, " Efficient and reliable scheme for nonlinear diffusion filtering," IEEE Trans. Image Processing, vol.7, no.3, pp.398–410, Mar. 1998. [76] Wu, X., "Lossless compression of continuous-tone images via context selection, quantization, and modeling," IEEE Trans. Image Processing, vol.6, no.5, pp.656-664, May 1997. [77] Wu, X. and N. Memon, "Context-based, adaptive, lossless image coding," IEEE Trans. Communications, vol.45, no.4, pp.437-444, Apr. 1997. [78] Wu, X. and N. Memon, "Context-based lossless interband compression - extending CALIC," IEEE Trans. Image Processing, vol.9, no.6, pp.994-1001, June 2000. [79] Wu, Y.-G., "Medical image compression by sampling DCT coefficients," IEEE Trans. Information Technology in Biomedicine, vol.6, no.1, pp.86-94, Mar. 2002. [80] Xu, M., P. M. Thompson, and A. W. Toga, "An adaptive level set segmentation on a triangulated mesh," IEEE Trans. Medical Imaging, vol.23, no.2, pp.191-201, Feb. 2004. [81] Yezzi, A. J., S. Kichenassamy, A. Kumar, P. Olver, and A. Tannenbaum, "A Geometric snake model for segmentation of medical imagery," IEEE Trans. Medical Imaging, vol.16, no.2, pp.199-209, Apr. 1997.
指導教授	曾定章(Din-Chang Tseng)	審核日期	2007-6-24
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare