臺灣博碩士論文加值系統

乳癌一直是全球婦女的主要死因之一。近年來，乳房彈性超音波已被使用來測量腫瘤彈性，醫生需要對腫瘤組織施與輕微的壓力以便取得一段動態彈性視訊，藉由腫瘤組織的位移得到軟硬資訊，最後，醫生將會從視訊中選出一張代表性的影像來協助腫瘤的診斷。此篇論文的目的為使用影像量化的方法來自動地選出最具代表性的一張影像，並且自動切割出腫瘤輪廓來擷取特徵診斷腫瘤。首先，根據腫瘤內部的一致性(信號雜訊比，SNRe)以及腫瘤與周圍正常組織的對比性(對比雜訊比，CNRe)分別取得兩種彈性影像品質比率來找到代表性的彈性影像，接著使用Level Set方法來切割出腫瘤輪廓，最後藉由腫瘤輪廓來擷取B-mode與彈性特徵，並且結合兩者來診斷腫瘤。本實驗中以151個經過病理驗證的病例進行測試，包含89個良性以及62個惡性的病例，並且使用本篇論文的方法所選之影像比較醫生所選的影像。經由實驗結果，當使用彈性特徵時，CNRe的準確率為82.12%，SNRe為82.12%，醫生選圖則是82.78%;當使用B-mode特徵時，CNRe的準確率為80.78%，SNRe為87.42%，醫生選圖則是84.11%;當結合B-mode與彈性特徵時，CNRe的準確率為86.09%，SNRe為90.07%，醫生選圖則是89.40%。因此，使用SNRe與CNRe所選擇的代表性影像可以取代醫生所選，並且將B-mode與彈性特徵結合時會有明顯的提升。

The breast cancer is always the main causes of death for women. In recent years, the sonoelastography has been used to measure the tumor strain. In the sonoelastography, the physicians need to lightly compress a tumor to obtain a dynamic elastographic image sequence. According to the displacement of the tumor, the tumor strain will be obtained on sonoelastography video. Finally, the physicians will choose the representative slice from the dynamic elastographic image sequence to diagnose the tumor. The purpose of this study is to use image quantification method to automatically choose a representative slice, and automatically segment the tumor contour to evaluate the features to diagnose the tumor. First, according to the uniformity inside the tumor (the signal to noise ratio, SNRe) or the contrast of the tumor and the surrounding normal tissue (contrast to noise ratio, CNRe), the two kinds of quality quantification methods will be used to select the representative slice. Then, the level set method is used to segment the tumor contour. Finally, the B-mode and elastography features by the tumor contour are extracted for diagnosis. Furthermore, the two kinds of features are combined to diagnose the tumors to improve the performance. In this study, 151 biopsy-proved sonoelastography composed of 89 benign and 62 malignant masses are used to evaluate the performance of the quantification methods and the representative slices selected by the proposed methods will be compared to the physician-selected slice. In the experiment result, as using elastography features, the diagnosis performance of accuracy is 82.12% (124/151) on representative slice of CNRe, 82.12% (124/151) on representative slice of SNRe, 82.78% (125/151) on the physician-selected slice; as using B-mode features, the diagnosis performance of accuracy is 80.79% (122/151) on representative slice of CNRe, 87.42% (132/151) on representative slice of SNRe, 84.11% (127/151) on the physician-selected slice; as combining the B-mode and elastography features, the diagnosis performance of accuracy is 86.09% (130/151) on representative slice of CNRe, 90.07% (136/151) on representative slice of SNRe, 89.40% (135/151) on the physician-selected slice. Therefore, the representative slice selected by SNRe and CNRe colud replace the physician-selected slice to reduce the physician’s load, and combining the B-mode and elastography features will increase the diagnosis performance.

口試委員會審定書 i
ACKNOWLEDGEMENTS ii
摘要 iv
Abstract vi
LIST OF FIGURES x
LIST OF TABLES xv
Chapter 1 Introduction 1
Chapter 2 Material 3
2.1 Patients 3
2.2 Elastography 3
Chapter 3 The Proposed Method 5
3.1 Elasticity Extraction 6
3.2 Representative Slice 7
3.2.2 SNR Image Quantification 7
3.2.3 CNR Image Quantification 9
3.3 Segmentation 11
3.3.1 The Contrast-enhanced Gradient Image 12
3.3.1.1 Sigmoid image filter 12
3.3.1.2 Gradient magnitude filter 13
3.3.2 Level Set Method 14
3.3.3 Hole Filling Using Morphology Closing Operation 15
3.4 Tumor Analysis 16
3.4.1 Computerized BI-RADS B-mode US Features 17
3.4.1.1 Shape 17
3.4.1.2 Orientation 18
3.4.1.3 Margin 19
3.4.1.4 Lesion boundary 20
3.4.1.5 Echo pattern 21
3.4.1.6 Posterior acoustic feature 22
3.4.1.7 GLCM texture features 23
3.4.2 Computerized Elastographic Features 25
3.4.2.1 Stiffness Ratio 25
3.4.2.2 Elasticity mean 26
3.4.2.3 Lesion boundary elasticity 26
Chapter 4 Experiment result 28
4.1 Statistic Analysis 29
4.2 Elastographic and B-mode Features analysis 30
4.3 Tumor Classification 33
4.4 Discussion 47
Chapter 5 Conclusion and Future Works 49
References 51

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