臺灣博碩士論文加值系統

乳癌一直是女性癌症中的主要死因，但是只要早期檢測與醫治就能大幅提高其治癒率。近年來，由於電腦輔助診斷系統發展快速，已經不只能偵測腫瘤，還能對腫瘤進行分析診斷，因此，對於病患的切片檢查次數就能相對減低。近年來，為了提供臨床上快速檢查的乳房攝影工具，因此研發新的自動全乳房超音波機器。在這篇論文中，使用了自動超音波影像來進行實驗。首先，先對腫瘤區域以等位函數法進行自動腫瘤切割，接著，依據切割出來的腫瘤輪廓進行灰階值共生矩陣的紋理分析，傳統形狀資訊分析，以及建立與腫瘤有最小距離相合的橢球模型，並作此橢球與腫瘤的異同點分析，我們即依據此三類的特徵分析來做為診斷的依據。本實驗中使用了147個經過病理驗證的腫瘤，其中包括了76個良性病例以及71個惡性病例，重複以不同種類組合的特徵，以邏輯回歸分析的方式，在留一交互驗證的規約下作準確性的測試。從實驗結果來看，橢球特徵和傳統形狀特徵相結合有較高的準確率，可以達到85.03%(125/147)，敏感性達到84.51%(60/71)，特異性85.53%(65/76)，ROC曲線面積0.9466，總合上述結果，我們相信自動全乳房超音波不僅能用來做乳房腫瘤的檢查，還能用來做已偵測出的腫瘤診斷。

In the past, breast cancer is the major cause of death for women among all kinds of cancer. But the curability of breast cancer can be greatly improved if a proper treatment is adopted after an early detection. In recent years, the computer-aided diagnosis systems have been developed rapidly and they can not only detect the tumors but also differentiate malignant tumors from benign ones. Hence the demand of the breast biopsy of the detected tumors might be further reduced. Recently, the new automated whole breast ultrasound (ABUS) machines have also been developed in order to provide a fast screening tool as the routine clinical used mammography. In this paper, the ABUS images are used for the diagnosis of tumors. At first, the three-dimensional (3-D) tumor contour is segmented by using the automated level-set segmentation method. 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 to classify the benign and malignant tumors. In the experiment, there are 147 pathologyproven cases, including 76 benign tumors and 71 malignant ones, are used to test the diagnosis performance of the logistic regression model with a leave-one-out cross validation based on the proposed features. From the experiment results, it is found that ellipsoid fitting features combined with traditional shape features can achieve a better performance with accuracy 85.03% (125/147), the sensitivity 84.51% (60/71), specificity 85.53% (65/76), and the area under the ROC curve Az 0.9466. Hence, the ABUS images could be used not only for screening the breast cancers but also diagnosing the detected tumors.

論文口試委員審定書 i
ACKNOWLEDGEMENTS ii
摘要 iii
ABSTRACT iv
TABLE OF CONTENTS vi
LIST OF FIGURES vii
LIST OF TABLES x
Chapter 1 Introduction 1
Chapter 2 Material 5
2.1 Patients and Lesion Characters 5
2.2 Data Acquisition 5
Chapter 3 The Proposed Method 8
3.1 Tumor Segmentation 9
3.1.1 Contrast-Enhanced Gradient Image 10
3.1.1.1 Sigmoid filter operation 11
3.1.1.2 Sigma filter 11
3.1.1.3 Gradient magnitude filter 12
3.1.2 Level set method 14
3.1.3 Hole filling using morphology closing operation 17
3.2 Feature Extraction 19
3.2.1 Texture-based Features 19
3.2.2 Shape Features 21
3.2.3 Ellipsoid Fitting Features 22
Chapter 4 Experimental Results and Discussion 27
4.1 Statistic Analysis 27
4.2 Feature Analysis 29
4.3 Tumor Classification 30
4.4 Discussion 43
Chapter 5 Conclusion 46
Reference 48

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