簡易檢索 / 詳目顯示

回結果列表
研究生： 周宜璇
Chou, Yi-Hsuan
論文名稱： 細胞核位置強化與細胞質分割基於統計動態輪廓模型應用於三倍頻顯微影像之細胞核質比分析
Nuclei Location Enhancement and Cytoplasm Segmentation Based on Statistical Active Contour Model for Nuclear-to-Cytoplasmic (NC) Ratio Analysis in Third Harmonic Generation Microscopy Image
指導教授： 李國君
Lee, Gwo-Giun
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 154
中文關鍵詞： 三倍頻顯微技術 、細胞定位 、細胞分割 、細胞核質比 、霍夫轉換 、動態輪廓模型 、蛇模型
外文關鍵詞： Third Harmonic Generation (THG), cell location, cell segmentation, Nuclear-to-Cytoplasmic ratio (NC ratio), Hough transform, active contour mocel, snake
相關次數： 點閱：157  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 為了建立一套電腦輔助診斷協定能夠提供定量與客觀的生物資訊，本篇論文提出的一個針對醫學影像分析系統的新細胞分割演算法，並且能夠有效地將它用在由大量的健康受試者所提供之體內虛擬切片影像。所提出的演算法包含謹慎的細胞核位置辨識方法，此方法經由考慮虛擬細胞的染色強度及透過霍夫轉換檢查細胞橢圓形狀特徵，並且，結合上真實細胞大小的知識以量化細胞結構去提高細胞核辨識的精確度，達到強化細胞核的定位。外圍細胞質邊界則是透過本篇論文所提出的統計壓力蛇模型所萃取出，此模型的驅動力是基於測定蛇輪廓運動的方向與細胞質上影像資訊之間局部統計資訊的相似性來決定適當的壓力大小，因此統計壓力蛇模型也能夠提供最佳的參數設定。在此篇論文的應用中，新的蛇模型中介紹的收斂控制因子(convergence control factor)與偏移參數(shifting parameter)克服了常見到的初始化及參數化的缺點。實驗結果顯示上述的演算法不只有很高的細胞定位及分割精確度，並且是一種具有處理不同種細胞影像能力之系統性和自適應性的方法。除此之外，所提出的方法在臨床診斷中亦展現出能夠非侵入式分析細胞核質比(NC ratio)以分辨皮膚疾病之異常細胞核質比之潛力。

    For the purpose of establishing a computer-aided diagnosis protocol to provide quantitative and objective bio-information, a new cell segmentation algorithm for biomedical image analysis system is proposed, and the algorithm could be effectively applied to process plenty of changeable in vivo virtual biopsy images provided by healthy individuals. The proposed approach includes cautious identification of the nuclei position by considering both virtual staining intensity and ellipse shape feature examining based on the Hough transform, besides, combining the knowledge of real cell size to quantify cellular structure to increase the accuracy of nuclear recognition, thus achieving nuclei location enhancement. For cytoplasm boundary extraction, we propose a novel approach called the statistical pressure snake, which is an optimal parameter setting snake driven by a pressure force that measuring the local statistics similarity between the snake contour movement and the image data of cytoplasm. In this new snake model, a convergence control factor and a shifting parameter are introduced to overcome the well-known drawbacks of initialization and parameterization on active contour model. Experimental results show that the aforementioned algorithm is not only demonstrated having high accuracy for cell positioning and cytoplasm segmentation, but also a systematic and adaptive approach with ability to process various kinds of cellular images. Moreover, the proposed approach also reveals potential for non-invasive analysis of cell Nuclear-to-Cytoplasmic ratio (NC ratio), which is significant for the differentiation of skin disease with abnormal NC ratio in clinical diagnosis.

    摘 要 i Abstract iii Table of Contents vii List of Tables xi List of Figures xiii Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Background Information 3 1.2.1 Traditional Biopsy Methodology 3 1.2.2 Optical in vivo Virtual Biopsy 3 1.2.3 Physical Background of Imaging 4 1.2.4 Setup of the Optical Imaging 5 1.2.5 Human Skin 6 1.3 Motivation 9 1.4 Organization of this Thesis 14 Chapter 2 Surveys of Related Works in the Literatures 15 Feature Extraction 15 2.1 Low-level Feature Extraction 16 2.1.1 First-order Edge Detection 16 2.1.2 Second-order Edge Detection 18 2.2 Scale Invariant Feature Transform 19 2.3 Histograms of Oriented Gradients 20 2.4 Template Matching 20 2.5 Hough Transform 22 2.5.1 Straight Line Detection 22 2.5.2 Circle Detection 25 2.5.3 General Limitations 26 2.5.4 Generalized Hough Transform 26 2.6 Radon Transform 27 Cell Segmentation 28 2.7 Intensity Thresholding 28 2.8 Region-Based Segmentation 28 2.8.1 Region Growing 29 2.8.2 Watershed Transformation 29 2.9 Clustering 30 2.9.1 Spectral Clustering 30 2.9.2 K-means Clustering 31 2.10 Deformable Models 32 2.10.1 Parametric Deformable Model 33 2.10.2 Geometric Deformable Models 39 Receiver Operating Characteristic 42 Chapter 3 Proposed Algorithms 47 Cell Segmentation and NC Ratio Analysis in Biomedical Imaging 47 3.1 Block Diagram 47 3.2 Biomedical Image Preprocessing 51 3.3 Nuclei Segmentation 53 3.3.1 Strict Nuclei Initialization 54 3.3.2 Ellipse Shape Description 55 3.3.3 Enhanced Ellipse Shape Description 68 3.4 Cytoplasm Segmentation 76 3.4.1 Basic of Active Contour Model － Snake 77 3.4.2 Definition of Snake Force 79 3.4.3 Snake Initialization 81 3.4.4 Cytoplasm Detection 82 3.5 Cell Size and NC Ratio Evaluation 93 Chapter 4 Experimental Results and Discussion 95 4.1 Experimental Results of Strict Nuclei Initialization 95 4.1.1 Qualitative Comparison 97 4.1.2 Quantitative Comparison 100 4.1.3 Comparison with Previous Works 108 4.2 Experimental Results of Cytoplasm Segmentation 111 4.2.1 Comparison with Common Cell Segmentation Approaches 129 4.2.2 Qualitative Comparison 133 4.2.3 Quantitative Comparison 139 Chapter 5 Conclusions and Future Works 143 5.1 Conclusions 143 5.2 Future Works 144 Acknowledgments 145 References 147

    [1] E. Vega‐Memije, N. M. De Larios, L. M. Waxtein, and L. Dominguez‐Soto, "Cytodiagnosis of cutaneous basal and squamous cell carcinoma," International journal of dermatology, vol. 39, pp. 116-120, 2000.
    [2] Y. Oram, O. Turhan, and N. Din, "Diagnostic value of cytology in basal cell and squamous cell carcinomas," International journal of dermatology, vol. 36, pp. 156-157, 1997.
    [3] C.-S. Hsieh, S.-U. Chen, Y.-W. Lee, Y.-S. Yang, and C.-K. Sun, "Higher harmonic generation microscopy of in vitro cultured mammal oocytes and embryos," Optics express, vol. 16, pp. 11574-11588, 2008.
    [4] C.-K. Sun, "Higher harmonic generation microscopy," in Microscopy Techniques, ed: Springer, 2005, pp. 17-56.
    [5] S.-Y. Chen, S.-U. Chen, H.-Y. Wu, W.-J. Lee, Y.-H. Liao, and C.-K. Sun, "In vivo virtual biopsy of human skin by using noninvasive higher harmonic generation microscopy," IEEE J. Sel. Top. Quantum Electron, vol. 16, pp. 478-492, 2010.
    [6] T. Y. Tsang, "Optical third-harmonic generation at interfaces," Physical Review A, vol. 52, p. 4116, 1995.
    [7] C.-K. Sun, S.-W. Chu, S.-Y. Chen, T.-H. Tsai, T.-M. Liu, C.-Y. Lin, et al., "Higher harmonic generation microscopy for developmental biology," Journal of structural biology, vol. 147, pp. 19-30, 2004.
    [8] Y.-H. Liao, S.-Y. Chen, S.-Y. Chou, P.-H. Wang, M.-R. Tsai, and C.-K. Sun, "Determination of chronological aging parameters in epidermal keratinocytes by in vivo harmonic generation microscopy," Biomedical optics express, vol. 4, pp. 77-88, 2013.
    [9] Y.-H. Liao, W.-C. Kuo, S.-Y. Chou, C.-S. Tsai, G.-L. Lin, M.-R. Tsai, et al., "Quantitative analysis of intrinsic skin aging in dermal papillae by in vivo harmonic generation microscopy," Biomedical optics express, vol. 5, pp. 3266-3279, 2014
    [10] C.-S. Cai, C.-F. Chen, G. G. Lee, G.-L. Lin, S.-Y. Chou, M.-R. Tsai, et al., "Density analysis of collagen fibers based on enhanced frangi filter in Second Harmonic Generation virtual biopsy images," in Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit & International Conference on, 2014, pp. 465-469.
    [11] C.-S. Cai, “Analysis of collagen fiber features in medical images”, M.S. thesis, Dept. Elect. Eng., National Cheng Kung Univ., Tainan, Taiwan, 2015.
    [12] M. T. McCann, J. A. Ozolek, C. A. Castro, B. Parvin, and J. Kovačević, "Automated Histology Analysis [opportunities for signal processing]," IEEE Signal Processing Mag., vol. 32, no. 1, pp. 78-87, Jan. 2015.
    [13] J. M. Prewitt, "Object enhancement and extraction," Picture processing and Psychopictorics, vol. 10, pp. 15-19, 1970.
    [14] L. S. Davis, "A survey of edge detection techniques," Computer graphics and image processing, vol. 4, pp. 248-270, 1975.
    [15] J. Canny, "A computational approach to edge detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 679-698, 1986.
    [16] D. G. Lowe, "Object recognition from local scale-invariant features," in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, 1999, pp. 1150-1157.
    [17] F. Mualla, S. Scholl, B. Sommerfeldt, A. Maier, and J. Hornegger, "Automatic cell detection in bright-field microscope images using SIFT, random forests, and hierarchical clustering," Medical Imaging, IEEE Transactions on, vol. 32, pp. 2274-2286, 2013.
    [18] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 886-893.
    [19] G. Apostolopoulos, S. V. Tsinopoulos, and E. Dermatas, "Identification the shape of biconcave Red Blood Cells using Histogram of Oriented Gradients and covariance features," in Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on, 2013, pp. 1-4.
    [20] J. P. Lewis, "Fast template matching," in Vision interface, 1995, pp. 15-19.
    [21] Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, "Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique," Medical Imaging, IEEE Transactions on, vol. 20, pp. 595-604, 2001.
    [22] K. Briechle and U. D. Hanebeck, "Template matching using fast normalized cross correlation," in Aerospace/Defense Sensing, Simulation, and Controls, 2001, pp. 95-102.
    [23] J. Illingworth and J. Kittler, "A survey of the Hough transform," Computer vision, graphics, and image processing, vol. 44, pp. 87-116, 1988.
    [24] R. O. Duda and P. E. Hart, "Use of the Hough transformation to detect lines and curves in pictures," Communications of the ACM, vol. 15, pp. 11-15, 1972.
    [25] D. Ioannou, W. Huda, and A. F. Laine, "Circle recognition through a 2D Hough transform and radius histogramming," Image and vision computing, vol. 17, pp. 15-26, 1999.
    [26] H. Yuen, J. Princen, J. Illingworth, and J. Kittler, "Comparative study of Hough transform methods for circle finding," Image and Vision Computing, vol. 8, pp. 71-77, 1990.
    [27] R. A. McLaughlin, "Randomized Hough transform: improved ellipse detection with comparison," Pattern Recognition Letters, vol. 19, pp. 299-305, 1998.
    [28] A. S. Aguado, M. Eugenia Montiel, and M. S. Nixon, "On using directional information for parameter space decomposition in ellipse detection," Pattern Recognition, vol. 29, pp. 369-381, 1996.
    [29] J. Illingworth and J. Kittler, "The adaptive Hough transform," Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 690-698, 1987.
    [30] S. Tsuji and F. Matsumoto, "Detection of ellipses by a modified Hough transformation," IEEE Transactions on Computers, vol. 27, pp. 777-781, 1978.
    [31] F. B. Tek, A. G. Dempster, and I. Kale, "Blood cell segmentation using minimum area watershed and circle radon transformations," in Mathematical Morphology: 40 Years On, ed: Springer, 2005, pp. 441-454.
    [32] C. Ortiz de Solorzano, R. Malladi, S. Lelievre, and S. Lockett, "Segmentation of nuclei and cells using membrane related protein markers," Journal of Microscopy, vol. 201, pp. 404-415, 2001.
    [33] H.-S. Wu, J. Barba, and J. Gil, "A parametric fitting algorithm for segmentation of cell images," Biomedical Engineering, IEEE Transactions on, vol. 45, pp. 400-407, 1998.
    [34] A. Garrido and N. Perez De La Blanca, "Applying deformable templates for cell image segmentation," Pattern Recognition, vol. 33, pp. 821-832, 2000.
    [35] D. H. Ballard, "Generalizing the Hough transform to detect arbitrary shapes," Pattern recognition, vol. 13, pp. 111-122, 1981.
    [36] S. R. Deans, "Hough transform from the Radon transform," Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 185-188, 1981.
    [37] D. V. Jadhav and R. S. Holambe, "Feature extraction using Radon and wavelet transforms with application to face recognition," Neurocomputing, vol. 72, pp. 1951-1959, 2009.
    [38] E. Meijering, "Cell segmentation: 50 years down the road [life sciences]," Signal Processing Magazine, IEEE, vol. 29, pp. 140-145, 2012.
    [39] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using MATLAB vol. 2: Gatesmark Publishing Knoxville, 2009.
    [40] M. M. Mokji and S. A. R. Abu Bakar, "Adaptive thresholding based on co-occurrence matrix edge information," in Modelling & Simulation, 2007. AMS'07. First Asia International Conference on, 2007, pp. 444-450.
    [41] M. Sezgin, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic imaging, vol. 13, pp. 146-168, 2004.
    [42] R. Adams and L. Bischof, "Seeded region growing," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, pp. 641-647, 1994.
    [43] J. M. Gauch, "Image segmentation and analysis via multiscale gradient watershed hierarchies," Image Processing, IEEE Transactions on, vol. 8, pp. 69-79, 1999.
    [44] F. Meyer, "Topographic distance and watershed lines," Signal processing, vol. 38, pp. 113-125, 1994.
    [45] F. Meyer and S. Beucher, "Morphological segmentation," Journal of visual communication and image representation, vol. 1, pp. 21-46, 1990.
    [46] A. Bleau and L. J. Leon, "Watershed-based segmentation and region merging," Computer Vision and Image Understanding, vol. 77, pp. 317-370, 2000.
    [47] U. Von Luxburg, "A tutorial on spectral clustering," Statistics and computing, vol. 17, pp. 395-416, 2007.
    [48] J. Shi and J. Malik, "Normalized cuts and image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, pp. 888-905, 2000.
    [49] A. K. Jain, "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol. 31, pp. 651-666, 2010.
    [50] R. Tibshirani, G. Walther, and T. Hastie, "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, pp. 411-423, 2001.
    [51] G. Hamerly and C. Elkan, "Alternatives to the k-means algorithm that find better clusterings," in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 600-607.
    [52] K. Teknomo. Kardi Teknomo’s page "Tutorials on similarity and distance,". Available: http://people.revoledu.com/kardi/tutorial/Similarity/index.html
    [53] C. Xu, D. L. Pham, and J. L. Prince, "Image segmentation using deformable models," Handbook of medical imaging, vol. 2, pp. 129-174, 2000.
    [54] T. McInerney and D. Terzopoulos, "Deformable models in medical image analysis: a survey," Medical image analysis, vol. 1, pp. 91-108, 1996.
    [55] M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International journal of computer vision, vol. 1, pp. 321-331, 1988.
    [56] R. Courant and D. Hilbert, Methods of mathematical physics vol. 1: CUP Archive, 1966.
    [57] L. D. Cohen, "On active contour models and balloons," CVGIP: Image understanding, vol. 53, pp. 211-218, 1991.
    [58] C. Xu and J. L. Prince, "Snakes, shapes, and gradient vector flow," Image Processing, IEEE Transactions on, vol. 7, pp. 359-369, 1998.
    [59] V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," International journal of computer vision, vol. 22, pp. 61-79, 1997.
    [60] S. Osher and N. Paragios, Geometric level set methods in imaging, vision, and graphics: Springer, 2003.
    [61] R. Kimmel, A. Amir, and A. M. Bruckstein, "Finding shortest paths on surfaces using level sets propagation," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 17, pp. 635-640, 1995.
    [62] J. A. Sethian, Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science vol. 3: Cambridge university press, 1999.
    [63] G. G. Lee, H.-H. Lin, M.-R. Tsai, S.-Y. Chou, W.-J. Lee, Y.-H. Liao, et al., "Automatic Cell Segmentation and Nuclear-to-Cytoplasmic Ratio Analysis for Third Harmonic Generated Microscopy Medical Images," Biomedical Circuits and Systems, IEEE Transactions on, vol. 7, pp. 158-168, 2013.
    [64] T. Lindeberg, "Feature detection with automatic scale selection," International journal of computer vision, vol. 30, pp. 79-116, 1998.
    [65] S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, et al., "Adaptive histogram equalization and its variations," Computer vision, graphics, and image processing, vol. 39, pp. 355-368, 1987.
    [66] J. S. Lim, "Two-dimensional signal and image processing," Englewood Cliffs, NJ, Prentice Hall, 1990, 710 p., vol. 1, 1990.
    [67] Z. Yue, A. Goshtasby, et al., "Automatic detection of rib borders in chest radiographs," IEEE Transactions on Medical Imaging, vol. 14, pp. 525-536, 1995.
    [68] Y. Xie and Q. Ji, "A new efficient ellipse detection method," in Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2002, pp. 957-960.
    [69] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, 3rd Edition, Pearson Education, 2008.
    [70] H. Kobatake and S. Hashimoto, "Convergence index filter for vector fields," Image Processing, IEEE Transactions on, vol. 8, pp. 1029-1038, 1999.
    [71] G. Borgefors, "Distance transformations in digital images," Computer vision, graphics, and image processing, vol. 34, pp. 344-371, 1986.
    [72] T. Fawcett, "An introduction to ROC analysis," Pattern recognition letters, vol. 27, pp. 861-874, 2006.
    [73] T. H. Wonnacott and R. J. Wonnacott, Introductory statistics vol. 19690: Wiley New York, 1972.
    [74] C. Wählby, I. M. SINTORN, F. Erlandsson, G. Borgefors, and E. Bengtsson, Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections," Journal of Microscopy, vol. 215, pp. 67-76, 2004.
    [75] Y. Al-Kofahi, W. Lassoued, W. Lee, and B. Roysam, "Improved automatic detection and segmentation of cell nuclei in histopathology images," Biomedical Engineering, IEEE Transactions on, vol. 57, pp. 841-852, 2010.
    [76] N. H. Mahmood and M. A. Mansor, "Red blood cells estimation using Hough transform technique," Signal & Image Processing: An International Journal (SIPIJ), vol. 3, pp. 53-64, 2012.
    [77] D. Walsh and A. E. Raftery, "Accurate and efficient curve detection in images: the importance sampling Hough transform," Pattern Recognition, vol. 35, pp. 1421-1431, 2002.
    [78] R. A. McLaughlin, "Randomized Hough transform: improved ellipse detection with comparison," Pattern Recognition Letters, vol. 19, pp. 299-305, 1998.
    [79] L. Bradbury and J. Wan, "A spectral k-means approach to bright-field cell image segmentation," in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, 2010, pp. 4748-4751.
    [80] S. Mohapatra and D. Patra, "Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images," in Systems in Medicine and Biology (ICSMB), 2010 International Conference on, 2010, pp. 49-54.
    [81] Q. Wu, F. Merchant, and K. Castleman, Microscope image processing: Academic press, 2010.
    [82] R. Delgado-Gonzalo, V. Uhlmann, D. Schmitter, and M. Unser, “Snakes on a plane [a perfect snap for bioimage analysis],” Signal Processing Magazine, IEEE, vol. 32, no. 1, pp. 41-48, Jan. 2015.

    無法下載圖示 校內：2025-01-01公開
    校外：不公開
    電子論文尚未授權公開，紙本請查館藏目錄
    QR CODE