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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張瑞峰 | |
dc.contributor.author | Day-Woei Wang | en |
dc.contributor.author | 王戴偉 | zh_TW |
dc.date.accessioned | 2021-06-13T07:04:13Z | - |
dc.date.available | 2016-08-02 | |
dc.date.copyright | 2011-08-02 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-07-22 | |
dc.identifier.citation | References
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Kuba, 'A 3D 6-subiteration thinning algorithm for extracting medial lines,' Pattern Recognition Letters, vol. 19, pp. 613-627, May 1998. [18] R. C. Gonzalez and R. E. Woods, Digital image processing, 3rd ed. Upper Saddle River, N.J.: Prentice Hall, 2008. [19] R. M. Haralick, et al., 'Textural Features for Image Classification,' Ieee Transactions on Systems Man and Cybernetics, vol. Smc3, pp. 610-621, 1973. [20] W. C. Shen, et al., 'Computer aided classification system for breast ultrasound based on Breast Imaging Reporting and Data System (BI-RADS),' Ultrasound Med Biol, vol. 33, pp. 1688-98, Nov 2007. [21] W. C. Shen, et al., 'Breast ultrasound computer-aided diagnosis using BI-RADS features,' Acad Radiol, vol. 14, pp. 928-39, Aug 2007. [22] E. Bribiesca, 'An easy measure of compactness for 2D and 3D shapes,' Pattern Recognition, vol. 41, pp. 543-554, Feb 2008. [23] K. K. Hunt, Breast cancer. New York: Springer-Verlag, 2001. [24] K. F. Mulchrone and K. R. Choudhury, 'Fitting an ellipse to an arbitrary shape: implications for strain analysis,' Journal of Structural Geology, vol. 26, pp. 143-153, 2004. [25] D. W. Hosmer and S. Lemeshow, Applied logistic regression, 2nd ed. New York: Wiley, 2000. [26] E. Alpaydin, Introduction to machine learning, 2nd ed. Cambridge, Mass.: MIT Press, 2010. [27] R. C. Sprinthall, Basic statistical analysis, 8th ed. Boston: Pearson Allyn & Bacon, 2007. [28] D. M. Parkin, et al., 'Estimating the world cancer burden: Globocan 2000,' Int J Cancer, vol. 94, pp. 153-6, Oct 15 2001. [29] N. Azizun, et al., 'Comparison of ER, PR and HER-2/neu (C-erb B 2) reactivity pattern with histologic grade, tumor size and lymph node status in breast cancer,' Asian Pac J Cancer Prev, vol. 9, pp. 553-6, Oct-Dec 2008. [30] N. Bhooshan, et al., 'Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers,' Radiology, vol. 254, pp. 680-690, Mar 2010. [31] B. Matkovic, et al., 'Immunohistochemical analysis of ER, PR, HER-2, CK 5/6, p63 and EGFR antigen expression in medullary breast cancer,' Tumori, vol. 94, pp. 838-844, Nov-Dec 2008. [32] J. H. Chen, et al., 'Estrogen receptor and breast MR imaging features: a correlation study,' J Magn Reson Imaging, vol. 27, pp. 825-33, Apr 2008. [33] R. F. Chang, et al., 'Rapid image stitching and computer-aided detection for multipass automated breast ultrasound,' Med Phys, vol. 37, pp. 2063-73, May 2010. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35673 | - |
dc.description.abstract | 在所有癌症當中,乳癌一直是女性死亡的主要原因之一。近年來,由於電腦輔助診斷系統快速的發展,現在它們不但可以偵測腫瘤的存在,甚至能夠判斷出腫瘤的良惡性,因此或許可以讓乳房病理切片診斷的必要性大幅下降。自從都卜勒超音波成功地偵測到血液的流動,腫瘤血管型態、腫瘤形狀和腫瘤紋理就變成為主要的研究課題。一般而言,和良性腫瘤相比,惡性腫瘤需要更多的血管來提供生長所需要的養分和氧氣,另外,惡性腫瘤的體積通常較大,表面比較不平整。在過去的研究中,通常只利用腫瘤本身或是腫瘤血管的特徵來診斷腫瘤的良惡性。在本篇論文中,我們提出的電腦輔助診斷系統將利用3D都卜勒超音波,同時獲得腫瘤及血管的資訊,並以此分類腫瘤。為了獲得腫瘤及血管的特徵,必須先將灰階影像利用水平集方法切割出腫瘤的輪廓,再利用三維細線化演算法取得血管骨幹。接著,腫瘤紋理特徵、腫瘤形狀特徵、腫瘤橢球特徵和血管特徵將會被取出。最後再以二元邏輯回歸的方式利用這些特徵來判斷乳房腫瘤的良惡性。實驗共分析82個乳房腫瘤病例,其中包含41個良性及41個惡性腫瘤。根據實驗結果,我們所提出的結合腫瘤及血管兩類特徵的準確率比只使用單一類特徵的好,可達到準確率85.37%、敏感性85.37%、專一性85.37%以及ROC曲線面積0.9104。 | zh_TW |
dc.description.abstract | The breast cancer is always a major cause of death for women among all kind of cancers. In recent years, the computer-aided diagnosis systems have been developed rapidly and they can not only detect the tumors but also distinguish malignant tumors from benign ones. Therefore, the need of the breast biopsy of the detected tumors might be further decreased. Since the blood flow is successfully detected with the conventional US image by the Doppler ultrasound (US), the studies of tumor vascularity, tumor morphology, and texture of tumor have played important roles to diagnose diseases of breast recently. In general, malignant tumors need more complex blood vessels to obtain sufficient nutrients for growing, and the volume and surface of malignant tumors are larger and more irregular than those of benign tumors. In the past, researches about Doppler US only used the characteristics based on the tumor or vascular information for diagnosing tumor. In this paper, we demonstrate a computer-aided diagnosis (CAD) system for three-dimensional (3-D) power Doppler breast US images that can quantify the characteristics of both vascularity and tumor. In order to obtain the features of tumor and vascularity, the level-set method is applied to segment the tumor margin and thinning approach is used to skeletonize the vascularity. Then, the features including the texture information based on co-occurrence matrix, shape information, and ellipsoid fitting information are extracted based on the segmented 3-D tumor contour, and the vascular morphology are quantified from the skeletonized vessels. The features are used to classify the benign and malignant tumors by the binary logistic regression model. In the experiment, 82 biopsy-proved lesions including 41 benign tumors and 41 malignant tumors are used to test the diagnosis performance of the proposed CAD system. From the experimental results, it is found that the features of tumor combined with vascular features has better performance than using single type of features. Moreover, the proposed method could achieve a high performance with the accuracy, sensitivity, specificity and Az value being 85.37% (70/82), 85.37% (35/41), 85.37% (35/41), and 0.9104, respectively. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T07:04:13Z (GMT). No. of bitstreams: 1 ntu-100-R98945024-1.pdf: 4998857 bytes, checksum: 0ddf9d9964e7cdc833f5a79d61f51133 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | TABLE OF CONTENTS
口試委員審定書 i ACKNOWLEDGEMENT ii 摘要 iii ABSTRACT iv TABLE OF CONTENTS vi LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 Material 3 2.1 Patients and Lesion Characters 3 2.2 Data Acquisition 3 Chapter 3 The Proposed 3-D PDUS Computer-aided Diagnosis 6 3.1 Tumor segmentation 7 3.1.1 The contrast-enhanced gradient image 7 3.1.1.1 Sigmoid filter 8 3.1.1.2 Gradient magnitude filter 10 3.1.2 Level set method 11 3.1.3 Hole filling 12 3.2 Vessel extraction 13 3.2.1 Vessel pixel extraction 13 3.2.2 Vessel skeleton extraction 14 3.3 Feature extraction 16 3.3.1 B-mode features 16 3.3.1.1 GLCM features 16 3.3.1.2 Shape features 17 3.3.1.3 Ellipsoid fitting features 18 3.3.2 Vascular features 20 3.3.2.1 Volume 20 3.3.2.2 Complexity 21 3.3.2.3 Length 21 3.3.2.4 Radius 21 3.3.2.5 Tortuosity 22 Chapter 4 Experimental Results and Discussion 25 4.1 Statistic analysis 26 4.2 Feature analysis 26 4.3 Tumor classification 27 4.4 Discussion 43 Chapter 5 Conclusion and Future Works 46 References 48 LIST OF FIGURES Fig. 1 Moving transducer mechanically to get 3-D volume. 4 Fig. 2 A set of 2-D Ultrasonic planes in a fanlike geometry. 4 Fig. 3 The screen of 4-D view program. The three sectional planes are perpendicular to each other. 5 Fig. 4 The flowchart of the proposed CAD system. 6 Fig. 5 Level set segmentation. 7 Fig. 6 (a) Original image (b) applying the sigmoid filter with α=8, β=24, (c) applying the gradient magnitude filter (d) applying the sigmoid filter with α=0.1, β=20, (e) applying the level set method (f) applying the closing operation (g) the tumor contour (h) overlapping with original image. 9 Fig. 7 The gradient masks (a) gx and (b) gy 10 Fig. 8 The zero set in a level set. The sign of ψ is decided by whether the position of the point is inside the zero level set (negative) or outside the zero level set (positive). 11 Fig. 9 The result of segmentation in 3-D view for Fig. 6. 12 Fig. 10 (a) The original vessel volume (b) the binary image after thresholding (c) the skeleton of vessels. 15 Fig. 11 Inside and outside regions. 19 Fig. 12 Inside and Outside angular ratio. 19 Fig. 13 The orientation feature Eangle denoted by θ. 20 Fig. 14 The distance metric DM. 22 Fig. 15 Inflection point 23 Fig. 16 The in-plane angle IPk between V1 and V2, and the torsional angle TPk, between N1 and N2. 23 Fig. 17 The ROC curves of the B-mode, vascular, and the combined features. 32 Fig. 18 A true positive case of invasive ductal carcinoma classified by the proposed combined features (a)(b)(c) original slices 36, 47, and 53 (d)(e)(f) segmented slices 36, 47, and 53 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are both malignant. 33 Fig. 19 A true positive case of invasive ductal carcinoma classified by the proposed combined features (a)(b)(c) original slices 50, 60, and 70 (d)(e)(f) segmented slices 50, 60, and 70 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are benign and malignant, respectively. 34 Fig. 20 A true positive case of invasive ductal carcinoma classified by the proposed combined features (a)(b)(c) original slices 27, 37, and 44 (d)(e)(f) segmented slices 27, 37, and 44 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are malignant and benign, respectively. 35 Fig. 21 A true positive case of invasive ductal carcinoma classified by the proposed combined features (a)(b)(c) original slices 56, 61, and 66 (d)(e)(f) segmented slices 56, 61, and 66 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are both benign. 36 Fig. 22 A true negative case of fibroadenoma classified by the proposed combined features (a)(b)(c) original slices 56, 62, and 69 (d)(e)(f) segmented slices 56, 62, and 69 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are both benign. 37 Fig. 23 A true negative case of fibroadenoma classified by the proposed combined features (a)(b)(c) original slices 21, 26, and 31 (d)(e)(f) segmented slices 21, 26, and 31 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are malignant and benign, respectively. 38 Fig. 24 A true negative case of papilloma classified by the proposed combined features (a)(b)(c) original slices 45, 50, and 55 (d)(e)(f) segmented slices 45, 50, and 55 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are benign and malignant, respectively. 39 Fig. 25 A true negative case of fibrocystic change classified by the proposed combined features (a)(b)(c) original slices 51, 56, and 61 (d)(e)(f) segmented slices 51, 56, and 61 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are both malignant. 40 Fig. 26 A false positive case of intraductal papilloma classified by the proposed combined features (a)(b)(c) original slices 41, 49, and 68 (d)(e)(f) segmented slices 41, 49, and 68 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are both malignant. 41 Fig. 27 A false negative case of ductal carcinoma in situ classified by the proposed combined features (a)(b)(c) original slices 53, 58, and 63 (d)(e)(f) segmented slices 53, 58, and 53 (g) segmented tumor and vascularity in 3-D view. The diagnosis results using B-mode and vascular features are both benign. 42 LIST OF TABLES Table 1 The mean value, standard deviation (SD), median value, p-value, of Student’s t test or Mann-Whitney U test of the B-mode features. 29 Table 2 The mean value, standard deviation (SD), median value, p-value, of Student’s t test or Mann-Whitney U test of the vascular features. 30 Table 3 The features selected by the backward elimination method for three groups of features. 30 Table 4 The result of the classification performance using logistic regression model and leave-one-out method for each feature set. 31 Table 5 The p-values of the five performance indices using chi-square test and Az value of ROC curve using z-test for the three different feature classes. 31 | |
dc.language.iso | en | |
dc.title | 3D都卜勒乳房超音波之腫瘤診斷 | zh_TW |
dc.title | Computer-Aided Diagnosis for 3-D Power Doppler Breast Ultrasound | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃俊升,張允中 | |
dc.subject.keyword | 都卜勒超音波,乳房,腫瘤,電腦輔助診斷,血管分佈, | zh_TW |
dc.subject.keyword | ultrasound,PDUS,vascularity,breast,tumor,computer-aided diagnosis, | en |
dc.relation.page | 50 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-07-22 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
顯示於系所單位: | 生醫電子與資訊學研究所 |
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