本論文主要研究不同超音波物理參數的整合於增進乳房超音波辨別良性和惡性腫瘤的診斷效能。整體分為三大主題，首先，在第二章中探討由灰階影像萃取的形態學參數和紋理參數，以及由超音波Nakagami參數影像萃取的散射子參數之間的關係與診斷效能。結果證實不同物理量參數的相關程度較低，以同時結合形態學參數、紋理特徵參數和Nakagami參數區分良性和惡性乳房腫瘤的效能最佳，其特異性和敏感度都可達到88%以上，且接受器工作曲線下面積有0.95。其次，於第三章中，將應用彈性成像的方法於超音波Nakagami參數影像上，藉由外部壓迫後觀察序列的超音波Nakagami參數影像所反映的局部散射子濃度變化，來說明腫瘤組織的軟硬程度，結果以彈性應變影像驗證良性腫瘤的確較惡性腫瘤軟，且良性腫瘤的序列超音波Nakagami參數影像變化程度較大，換句話說，序列超音波Nakagami參數影像的變化程度可以間接描述組織的相對軟硬。最後，第四章則是延伸應變複合影像的概念於超音波Nakagami參數影像，於壓迫後，平均複合多張超音波Nakagami參數影像，藉由同時考慮多張影像的結果來增進描述腫瘤特徵的準確性，應變複合超音波Nakagami參數影像可以同時獲得腫瘤的散射子特性和軟硬程度。未來可將本論文所提及的技術應用於三維乳房超音波和其他部位進行病灶的診斷。

The primary goal of this work is to exploit the physical characteristics of feature type for better classifying breast tumors using ultrasound. The main topics can be divided to three certain parts. In chapter 2, ultrasound B-scan-based morphological and texture analysis and Nakagami parametric imaging were proposed to characterize breast tumors. These feature categories of ultrasound tissue characterization supplied information on different physical characteristics of breast tumors, by combining the above methods was expected to provide more clues for classifying breast tumors. The empirical results indicated that the combination of morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter resulted in the specificity and sensitivity both exceeded 88%, and the area under ROC curve of 0.95. In chapter 3, the feasibility of applying the elasticity imaging method to Nakagami imaging was investigated for visualizing the local redistributions of scatterers in a scattering medium with different stiffnesses. The preliminary results show the concept of the elasticity and scatterer characterizations being functionally complementary in classifying breast tumors. Consequently, the sequential Nakagami image frames obtained from different strain conditions would simply represent the relative tissue stiffness. In chapter 4, the use of a strain-compounding technique with Nakagami imaging was presented to identify breast lesions, which was regarded as strain-compounding in the Nakagami domain to provide a new parameter associated with the scatterers and stiffness of tissues. Combining information from multiple Nakagami images obtained under different strain conditions can be useful to improve the ability to interpret the characteristics of breast tumors. Potential applications of these proposed imaging techniques include extending to the three-dimensional ultrasound for classifying different stages and grades of breast tumors, and assisting abdominal or musculoskeletal ultrasound for detecting characteristic symptoms and abnormalities.

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