臺灣博碩士論文加值系統

乳癌是女性最普遍的惡性腫瘤之一，而且一直以來是世界各地女性的主要死因。許多研究指出高乳房密度的女性較低乳房密度的女性有更高的罹患乳癌風險。因此，乳房密度被認為在乳癌風險預測上具有極大的意義。大多數的乳房密度研究都是使用乳房X光影像。由於2維的乳房X光影像在密度分析上的變異較大，所以在最近的一些研究中，3維核磁共振攝影的使用已被當作能取得更為適當結果的主要途徑。然而，本篇論文將會在乳房X光影像中使用3維的方法來計算乳房密度。此方法是基於乳房的組成能分為脂肪組織和腺體組織兩種。利用X光在兩種組織具有不同的衰減係數以取得組成比例。再將乳房X光影像得到的密度分析結果與乳房核磁共振影像所取得的結果作比較。來自17個病人的實驗結果顯示乳房X光影像和乳房核磁共振影像兩者所得到的結果有高度相關性。此外，本篇論文所使用的乳房密度計算的方法也應用在39個病人的化學預防治療實驗結果中，做為追蹤乳房密度的變化情形。其中32個病例經過治療後的腺體體積出現下降的情形，其體積下降的百分比為13.40％。然而，有7個病例的腺體體積在經過化學預防治療後上升了5.72%，其中6個上升的病例其治療時間較短（少於12個月）。

Breast cancer is one of the most common malignancies in women and it continues to be a major cause of death among women worldwide. Several studies have shown that the women with dense breasts are at higher risk than the women whose breasts are less dense. Hence, breast density has to be accepted as a clinically highly significant predictor of breast cancer risk. Most studies of breast density are performed on the mammographic images. Because the two-dimensional (2-D) mammographic density shows large variability, the three-dimension (3-D) breast magnetic resonance imaging (MRI) has been used in some recent studies for achieving more reasonable consistency. In this paper, we propose a three-dimension (3-D) method to estimate the breast density with digital mammograms. This method is based on the assumption that the composition of the breast contains only two types, the fat tissue and the fibroglandular tissue. The linear attenuation coefficients of these tissues are used to obtain the composition at a given pixel. The density estimations of the mammographic images are compared with the results in the MRI. The validation between the MRI and the digital mammograms using 17 cases has been performed and shows a high correlation. Also, the proposed volumetric breast density estimation using the mammographic images is applied to evaluate the breast density change for 39 patients with the chemoprevention treatments. In 32 of 39 cases, the fibroglandular volume after treatment is reduced and the fibroglandular volume reduction is 13.40%. However, there are 7 cases with their fibroglandular volume increasing of 5.72% after the chemoprevention and 6 of 7 increasing cases receive shorter treatment (less than 12 months).

ACKNOWLEDGEMENTS ii
摘要 iii
ABSTRACT iv
TABLE OF CONTENT vi
LIST OF FIGURES vii
LIST OF TABLES ix
Chapter 1 Introduction 1
Chapter 2 Materials 4
Chapter 3 The Density Analysis Method 5
3.1 Unprocessed Digital Mammograms 6
3.2 Breast Segmentation 8
3.3 Fat Attenuation Coefficient 10
3.4 Fibroglandular Attenuation Coefficient 16
3.5 Volume and Density 17
Chapter 4 Experiment and Result 20
4.1 MRI and Mammographic Densities 20
4.2 Chemoprevention Density Change Using Processed Mammographic Images 21
4.3 Chemoprevention Density Change Using Unprocessed Mammographic Images 36
4.4 Discussion 52
Chapter 5 Conclusion and Future Works 55
References 58

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