臺灣博碩士論文加值系統

由於乳房超音波影像具有許多斑點、雜訊及低對比的特性，因此很難使用單一方法來完整的分割出腫瘤輪廓。本論文提出一個尋找初始輪廓的方法，以提升距離正規劃水平集演算法(DRLSE)用於分割乳房超音波影像的效果，特別針對鈕采紋學姐提出的演算法做改善。其演算法主要是透過取得影像中的區域最小值來得到初始輪廓，但以區域最小值作為尋找初始輪廓的方法相當依賴腫瘤內部的結構，若影像中有過多組織紋理將對分割結果產生極大的影響。因此本論文以影像平滑化的方式，消除腫瘤內部的弱邊界並調整像素值分布，配合自動選取閥值做二值化的方式來得到面積更大的初始輪廓，此方法能大幅降低DRLSE的疊代次數以及運算耗費之時間。除此之外，二值化流程能改善找不到局部最小值的影像，未能找到初始輪廓的影像，通常是因為影像中的音響陰影和腫瘤區塊之像素值過於接近。因此本論文針對這些影像，於二值化的步驟後做區域再分割，以區塊內的像素值與長度變化將分割出的乳房組織去除。之後配合形態學特徵、位置以及改良之平均像素值差，對排名公式做修改，藉此提升所選中之候選區塊為腫瘤的機會。最後透過執行DRLSE的演化使輪廓更貼近腫瘤邊緣。
為了評估分割的結果，本論文使用ME、RFAE以及MHD三項錯誤指標來比較不同方法的分割結果，與內縮法、外擴法以及學姐提出的演算法相比。本論文的方法基本上在錯誤評估的表現較好，此外，本論文的初始輪廓之面積較大，大幅降低了DRLSE的疊代次數，當資料庫越大時，本方法能夠更快速的處理影像。
如上所述，本論文提出的方法具有以下優點:其一，不需要配合每張超音波影像中的腫瘤位置以及輪廓來手動設定不同的初始位置；其二，本方法所得之初始輪廓面積較大，能夠大幅降低進行疊代所需的次數以及時間，節省大量運算所需的貯存空間；其三，使用本論文所提出之方法尋找到的初始輪廓位置，若原始影像的對比度大，確實可以幫助後續的DRLSE疊代提升效果，更靠近腫瘤輪廓位置。

Due to the speckle noise and low contrast in breast ultrasound images, it is hard to locate the contour of the tumor by using a single method. In this thesis, a new method for finding an initial contour is proposed, which can improve the result of DRLSE on the segmentation of BUS images.
The new method focuses on improving the algorithm proposed by Tsai-Wen Niu, which is a way to search an initial contour based on the local minimum in the images. When the BUS images contain calcification, it is possible to fail in searching of initial contour through such algorithm, hence leading to a poor segmentation result when the initial contour is on the wrong place. Therefore, we acquire a bigger initial contour by using a series of image smoothing methods and binarization, which can eliminate the weak edges and adjust the contrast in BUS images. In addition, some images without local minimum can be successfully detected by using the proposed method. However, the pixel value in these images are similar. It might be hard to accurately separate the tumor region from non-tumor region by the difference of pixel values. These obstacles are conquered by calculating the difference of length and pixel value in the suspect region. The ranking outcome is improved by using the morphological features. After applying DRLSE, our initial contour can reach the tumor region more accurately.
To evaluate the result of segmentation, it is compared with the outcome of DRLSE obtained from different initial contours proposed by Tsai-Wen Niu, expansion DRLSE method, and contraction DRLSE method using three evaluation metrics, including ME, RFAE and MHD. The experimental results indicate that the proposed method is basically better than the other methods. However, the initial contour might contain non-tumor region when the edge of the tumor’s boundary is too ambiguous; even so, the proposed method drastically reduce the number of DRLSE iteration and computation time.
According to the experimental results, the proposed method has three advantages over the other methods. First, it sets the initial contour automatically which is more efficient than setting the initial contour manually. Second, the region of the initial contour is much bigger than those obtained by the other methods, which can reduce the computation time and the number of DRLSE iteration. Third, if the tumor boundary is distinct, the new initial contour can improve the segmentation result of DRLSE.

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 vii
表目錄 x
第一章 簡介 1
1.1 前言 1
1.2 文獻回顧 1
1.2.1 Histogram Thresholding Method 2
1.2.2 Morphological Method 3
1.3 研究目的 6
1.4 論文架構 6
第二章 理論與方法 7
2.1 系統流程圖 7
2.2 影像前處理 8
2.2.1 Guided Image Filtering 8
2.2.2 Histogram Equalization 12
2.2.3 Smoothing via L0 Gradient Minimization 14
2.2.4 前處理步驟 18
2.3 尋找初始輪廓 20
2.3.1 影像二值化 22
2.3.2 去除脂肪區塊 25
2.3.3 去除候選區域與邊界相連的部分 29
2.3.4 候選區域排名 36
2.4 以DRLSE演化初始輪廓至腫瘤邊緣 39
2.4.1 傳統Level Set 39
2.4.2 距離正規化水平集演算法(DRLSE) 41
2.4.3 DRLSE疊代次數自動化 44
第三章 實驗結果 46
3.1 Data set 46
3.2 錯誤測量指標(MOEs) 49
3.2.1 ME 49
3.2.2 RFAE 49
3.2.3 MHD 50
3.3 實驗結果與討論 50
3.3.1 初始輪廓 51
3.3.2 分割結果 61
第四章 結論與未來展望 71
4.1 結論 71
4.2 未來展望 72
參考文獻 73

[1] 行政院衛生福利部, "民國104年死因統計年報, " 2015.
[2] O'Driscoll, D., et al. "Screening with breast ultrasound in a population at moderate risk due to family history." Journal of medical screening 8.2 (2001): 106-109.
[3] Zhou, Zhuhuang, et al. "Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts." Ultrasonic imaging 36.4 (2014): 256-276.
[4] Liu, Xu, Zhimin Huo, and Jiwu Zhang. "Automated segmentation of breast lesions in ultrasound images." 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
[5] Zhou, Shichong, et al. "Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image." Biomedical Signal Processing and Control 8.6 (2013): 688-696.
[6] Zhou, Zhuhuang, et al. "Classification of benign and malignant breast tumors in ultrasound images with posterior acoustic shadowing using half-contour features." Journal of medical and biological engineering 35.2 (2015): 178-187.
[7] Zhang, Su, et al. "Automatic feature extraction and analysis on breast ultrasound images." International Conference on Intelligent Computing. Springer Berlin Heidelberg, 2007.
[8] Telagarapu, Prabhakar, and S. Poonguzhali. "Analysis of contourlet texture feature extraction to classify the benign and malignant tumors from breast ultrasound images." International Journal of Engineering and Technology 6.1 (2014): 239-305.
[9] Vapnik, Vladimir. The nature of statistical learning theory. Springer Science & Business Media, 2013.
[10] Chen, Guan Lin, and Chia Yen Lee. "Iterative Morphology-Based Segmentation of Breast Tumors in Ultrasound Images." Computer, Consumer and Control (IS3C), 2014 International Symposium on. IEEE, 2014.
[11] 鈕采紋, "以基於形態學得到的初始輪廓之距離正規化水平集演算法做超音波乳房影像腫瘤區塊自動化切割, " 國立清華大學電機工程學系, 2016.
[12] Yap, Moi Hoon, Eran A. Edirisinghe, and Helmut E. Bez. "Fully automatic lesion boundary detection in ultrasound breast images." Medical Imaging. International Society for Optics and Photonics, 2007.
[13] Banimelhem, Omar, and Yahya Ahmed Yahya. "Multi-thresholding image segmentation using genetic algorithm." Computer Engineering, and Applied Computing. World Congress in Computer Science. 2011.
[14] Joo, Segyeong, Woo Kyung Moon, and Hee Chan Kim. "Computer-aided diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network." The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[15] Gonzalez, Rafael C., and Richard E. Woods. "Digital image processing." Prentice Hall (2008).
[16] Haralick, Robert M., Stanley R. Sternberg, and Xinhua Zhuang. "Image analysis using mathematical morphology." IEEE transactions on pattern analysis and machine intelligence 4 (1987): 532-550.
[17] Xu, Li, et al. "Image smoothing via L0 gradient minimization." ACM Transactions on Graphics (TOG). Vol. 30. No. 6. ACM, 2011.
[18] He, Kaiming, Jian Sun, and Xiaoou Tang. "Guided image filtering." IEEE transactions on pattern analysis and machine intelligence 35.6 (2013): 1397-1409.
[19] Draper, Norman R., and Harry Smith. "An introduction to nonlinear estimation." Applied Regression Analysis, Third Edition (1981): 505-565
[20] Tomasi, Carlo, and Roberto Manduchi. "Bilateral filtering for gray and color images." Computer Vision, 1998. Sixth International Conference on. IEEE, 1998.
[21] Pizer, Stephen M., et al. "Adaptive histogram equalization and its variations." Computer vision, graphics, and image processing 39.3 (1987): 355-368.
[22] Shan, Juan, H. D. Cheng, and Yuxuan Wang. "Completely automated segmentation approach for breast ultrasound images using multiple-domain features." Ultrasound in medicine & biology 38.2 (2012): 262-275.
[23] Yap, Moi Hoon, Eran A. Edirisinghe, and Helmut E. Bez. "A novel algorithm for initial lesion detection in ultrasound breast images." Journal of Applied Clinical Medical Physics 9.4 (2008): 181-199.
[24] Wu, Wen-Jie, Shih-Wei Lin, and Woo Kyung Moon. "Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images." Computerized Medical Imaging and Graphics 36.8 (2012): 627-633.
[25] Huang, Y‐L., et al. "Computer‐aided diagnosis using morphological features for classifying breast lesions on ultrasound." Ultrasound in Obstetrics & Gynecology 32.4 (2008): 565-572.
[26] https://hholdorf.com/2017/02/06/breast-sonography-lecture-5-part-2-module-3/
[27] Bowman, Adrian W., and Adelchi Azzalini. Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. Vol. 18. OUP Oxford, 1997.
[28] Osher, Stanley, and James A. Sethian. "Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations." Journal of computational physics 79.1 (1988): 12-49.
[29] 吳俊緯, "結合圖形邊界地圖資訊的距離正規化水平集演算法-用於全自動的指骨輪廓圈選研究, " 國立清華大學電機工程學系, (2014).
[30] Osher, Stanley, and Ronald Fedkiw. Level set methods and dynamic implicit surfaces. Vol. 153. Springer Science & Business Media, 2006.
[31] Sussman, Mark, et al. "An improved level set method for incompressible two-phase flows." Computers & Fluids 27.5 (1998): 663-680.
[32] Li, Chunming, et al. "Level set evolution without re-initialization: a new variational formulation." 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. IEEE, 2005.
[33] Li, Chunming, et al. "Distance regularized level set evolution and its application to image segmentation." IEEE transactions on image processing 19.12 (2010): 3243-3254.
[34] G. Gilboa, N. Sochen and Y. Y. Zeevi, "Forward-and-backward diffusion processes for adaptive image enhancement and denoising," Image Processing, vol. 11, no. 7, pp. 689-703, 7 2002.
[35] Sezgin, Mehmet. "Survey over image thresholding techniques and quantitative performance evaluation." Journal of Electronic imaging 13.1 (2004): 146-168.
[36] Liu, Chen-Chung, and Pei-Chung Chung. "Objects extraction algorithm of color image using adaptive forecasting filters created automatically." International Journal of Innovative Computing, Information and Control 7.10 (2011): 5771-5787.