因應東方女性罹患乳癌的比例日漸攀升，為了提升早期的治癒率，影像診斷系統扮演不可或缺的角色，針對東方女性的乳房較小巧且緻密而言，比起西方女性更依賴於乳房超音波的診斷。目前雖有許多研究利用乳房超音波的電腦輔助診斷系統幫助醫師辨別乳房腫瘤，但此方法仍會受限於儀器和人為操作因素；另有一派學者提出分析乳房超音波的原始射頻訊號，已證實Nakagami 分佈可描述乳房超音波訊號特性，也能有效區分良性和惡性的腫瘤，但仍未有較完整的一套方法，且Nakagami 參數影像的解析度較差。
因此，我們希望結合灰階影像的輪廓特徵和原始射頻訊號的資訊做為新的診斷工具，由灰階影像可以獲取乳房腫瘤的外型，而Nakagami 參數影像則可以區分腫瘤內部的成分，由不同的物理量一起探討乳房腫瘤的分類。傳統B-mode 影像前置處理，我們提出新的參數MSR(Mean to Standard deviation Ratio)做為增強灰階影像對比的加權因子，且發現MSR 的分佈能夠區分乳房病灶和正常乳腺組織，便採用雙峰高斯分佈擬合其直方圖，以最佳整體臨界值法獲取二值化影像，再利用增強對比後的灰階影像和二值化影像進行自動化輪廓圈選，最後，萃取腫瘤輪廓並採用6 種輪廓特徵參數，驗證其惡性腫瘤的輪廓不規則度會高於良性腫瘤。另一方面，針對Nakagami 參數進行探討，採用先前研究提出的Nakagami參數成像方法，並觀測不同乳房病灶其Nakagami 參數影像上ROI 內的平均Nakagami 參數，可以區分出纖維囊腫、脂肪、及實質性腫瘤，且統計出良性腫瘤的平均Nakagami 參數會高於惡性腫瘤。
利用Fuzzy C-means 執行所有特徵參數的群聚分析，證實結合輪廓特徵參數和Nakagami 參數的方法分類乳房腫瘤，兩者之間可以相輔相成，表現最好的輪廓特徵參數和平均Nakagami 參數結合後，其診斷效率的準確度為81.7%、敏感度為80%、特異性為83%。

Breast cancer is the most common cancer in women worldwide. According to the lately statistics in Taiwan, the mortality from breast cancer has become the fourth of cancerous diseases among women. In addition, younger women tend to have dense breasts and Asian women tend to have denser ones. Not only ultrasound is capable of detecting masses even in dense breasts, but also more convenient, safer, and forming the images in real-time tool for patient in regularly physical examination. Computer-aided diagnosis (CAD) has been used to discriminate between benign and malignant tumor for ultrasonic B-mode scans, but this method makes the classification largely dependent on the skill of the operator. Several studies have shown that the Nakagami parameter estimated from the ultrasonic backscattered signals can be used to assist conventional B-mode scanning when classifying breast tumors.
Hence, we propose to combine ultrasonic B-mode scans with Nakagami parametric image for categorizing breast masses. We expect to acquire boundary feature and internal components of breast masses from B-mode scans and Nakagami parametric image. For boundary feature, irregular degree of contour in malignant tumor is higher than benign tumor. For internal components, the average Nakagami parameter of malignant tumor is lower than benign tumor.
We used Fuzzy C-means (FCM) to separate malignant cluster and benign cluster by all parameters. The average Nakagami parameter and contour features provide complementary characteristics in diagnosis ultrasound breast tumor image. The best efficiency is that accuracy is 81.7 %, sensitivity is 80 %, and specificity is 83 %.

摘要
誌謝
目錄
圖索引
表索引
第一章: 緒論
1.1.研究動機與目的
1.2.乳房超音波
1.3.電腦輔助診斷系統文獻回顧
1.4.腫瘤分割文獻回顧
1.5.Nakagami統計應用於乳房超音波文獻回顧
1.6.論文架構
第二章: 方法流程
2.1.資料擷取
2.1.1.乳房仿體
2.1.2.臨床資料
2.2.模擬乳房超音波影像
2.3.乳房超音波影像自動圈選腫瘤輪廓
2.3.1.影像前處理
2.3.2.最佳整體臨界值法
2.3.3.圓盤擴張法
2.3.4.自動圈選輪廓驗證
2.3.5.輪廓特徵萃取
2.4.超音波Nakagami參數成像
2.4.1.雷利分佈的模型
2.4.2.Nakagami統計分佈原理
2.4.3.Nakagami參數成像方法
第三章: 實驗結果
3.1.比較手動圈選原始影像和Enhancement影像結果
3.2.圓盤擴張法自動圈選結果
3.2.1.模擬影像驗證
3.2.2.臨床影像驗證
3.2.2.1.GVF- snake演算法
3.2.2.2.比較圈選結果
3.3.輪廓特徵參數
3.3.1.模擬影像結果
3.3.2.臨床影像結果
3.4.平均Nakagami參數
3.4.1.乳房仿體結果
3.4.2.臨床結果
第四章: 統計分類結果
4.1.接受器操作特性曲線(ROC curve)分析不同參數的分類能力
4.2.模糊C-means分群法(FCM)
第五章: 討論、結論與未來研究方向
5.1.討論影像前置處理
5.2.討論圓盤擴張法
5.3.討論Nakagami成像
5.4.結論
5.5.未來研究方向
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