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研究生:陳顥仁
研究生(外文):Hao-Jen Chen
論文名稱:徒手式超音波之全乳房電腦輔助篩檢
論文名稱(外文):Whole Breast Computer-aided Screening Using Free-hand Ultrasound
指導教授:張瑞峰張瑞峰引用關係
指導教授(外文):Ruey-Feng Chang
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:67
中文關鍵詞:超音波徒手式乳房腫瘤偵測篩檢冠狀視角
外文關鍵詞:ultrasoundfree-handbreast cancerdetectionscreeningcoronal view
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近十年來,由於超音波的便利性及對人體較無傷害,超音波檢測在對乳癌的篩檢及診斷上一直扮演著很重要的腳色。不過由於超音波造像的品質相當依賴檢測人員的操作經驗,所以難免會因為檢測人員本身的疲勞問題或是超音波影像的品質問題而導致一些失查。近年來,許多電腦輔助偵測系統方面的研究,提供了醫生在進行乳房腫瘤篩檢時的便利性。但這些電腦輔助偵測系統往往有些限制,例如需要人為的涉入而非全自動系統。在本篇論文中,對於整個乳房的掃描影像,我們提出了一種全新的自動偵測方式去找出所有可能有問題的地方。首先,由於全乳房掃描會產生大量的超音波影像,所以我們採用了取樣的技術去縮短系統的處理時間。接著,我們會自動找出影像中乳腺的區域,因為腫瘤通常都是長在這個區域裡。由於超音波影像本身具有強烈的雜訊,我們會使用非全方位擴散模糊的方式來過濾斑點雜訊,並以細棒演算法及灰階分切的技術來強化影像的資訊。在經過分水嶺演算法將影像分割成許多區域後,我們會根據腫瘤的特性來定義一些判斷的條件,並利用這些條件篩選出有可能為腫瘤組織的區域。其中,我們會採用可疑組織的冠狀視角來作為一個有力的篩選條件。在作為測試的13個病例中,幾乎所有的腫瘤跟水泡都能被正確找到,而平均每個病例會產生兩個誤判。從實驗的結果,我們驗證了本篇論文所提出的系統的準確性。

In the last decade, ultrasound imaging plays an important role in the field of breast cancer diagnosis because of its convenience and non-invasive. On the other hand, ultrasound is highly operator-dependent and some oversights would happen due to the tiredness of the physician or the poor image quality. Recently, the development of computer-aided diagnosis (CAD) provides a convenient way for doctors in detecting breast cancer using ultrasound images. However, the previous CAD systems have some limits with the requirements of human intervention. Hence, in this paper a novel automatic CAD system is proposed to find the suspicious frames among the whole breast ultrasound images. At first, the subsamping procedures for time-reducing are applied due to the huge amounts of whole breast US images. Then, the mammary zone where the tumors are usually located is identified through an automatic decision method. The poor image quality due to the speckle noise would be improved by using the anisotropic diffusion filter, and the image information would be enhanced by using the stick operator and the gray-level slicing technique. After applying the watershed segmentation, the suspicious segmented regions can be identified through several criteria defined according to the statistic and geometric features of a tumor. Also, a special feature, the coronal view of a suspicious region, will be used to improve the detection accuracy. By examining the US test database consisted of 13 cases, almost all the tumors and cysts could successfully be detected when an average of two false positive for each case is produced. The experimental results prove the accuracy of this purposed system.

TABLE OF CONTENTS
摘 要 i
ABSTRACT iii
ACKNOWLEDGEMENTS v
LIST OF FIGURE vii
LIST OF TABLES xii
Chapter 1 Introduction 1
Chapter 2 Free-hand Whole Breast Ultrasound and Related Pre- processing Techniques 5
2.1 Free-hand Whole Breast Ultrasound 5
2.2 Related Pre-processing Techniques 7
2.2.1 Anisotropic Diffusion Filter 8
2.2.2 Stick Detection 10
2.2.3 Gray-level Slicing 11
Chapter 3 Automatic Screening System 14
3.1 Pass Separation 15
3.2 Subsampling Procedure 16
3.2.1 Temporal Subsampling 17
3.2.2 Spatial Subsampling 18
3.3 ROI Detection 20
3.4 Image Segmentation 22
3.5 Tumor Criteria 24
3.5.1 Dark Criterion 24
3.5.2 Uniform Criterion 25
3.5.3 Width-Height Ratio Criterion 25
3.5.4 Area Size Criterion 26
3.5.5 Irregular Region Criterion 27
3.5.6 Coronal Area Size Criterion 30
3.5.7 Region Continuity 33
3.6 Suspicious Object Tracking in Skipped Images 34
Chapter 4 Experimental Results 37
4.1 Experimental Results 37
4.2 Discussions 59
Chapter 5 Conclusion 60
References 62

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