Nakagami參數影像可以區分組織中散射子濃度的不同，並且已被證實有助於乳房腫瘤分類。但此方法受限於單張影像所選取的腫瘤切面不同而影響分類的結果，因此本研究提出將應變複合技術應用於Nakagami影像，同時考慮多張Nakagami影像的資訊，排除單張影像可能造成的誤判，以提升乳房腫瘤分類的效能。而應變複合法已被提出應用於傳統B-scan影像，其取像原理為利用外力使散射子產生位移，因而得到不同的斑點分佈，以達到抑制斑點雜訊的效果。但實際上位移的多寡是根據組織的軟硬程度而有所不同，且會造成組織中散射子分佈的改變，因此我們可以藉由觀察連續的Nakagami影像之間散射子濃度的變化，以變化的多寡得知腫瘤的軟硬程度，最後將多張影像加以平均而得到Nakagami應變複合影像。比較Nakagami原始影像和複合影像時，可發現兩種影像之間散射子分佈的差異可用來區分不同軟硬程度的組織。而臨床上，惡性乳房腫瘤由於組織纖維增生的關係，因此較良性腫瘤硬，故我們提出將此項新的成像技術應用於乳房腫瘤分類並且利用接受器操作特性曲線(receiver operating characteristic curve, ROC curve)評估其分類效能，其中Az為0.97±0.02，準確度(accuracy)為96%，敏感度(sensitivity)為100%，特異性(specificity)則為92%。
總括而言，Nakagami應變複合影像可藉由觀察腫瘤內部Nakagami參數變化的程度而得知腫瘤的軟硬程度，因此本研究所提出的方法，具有成為電腦輔助診斷工具幫助診斷乳房腫瘤的潛力。

Nakagami parametric image has been used to discriminate different scatterer concentrations, and furthermore investigated to efficiently classify breast masses. However, the different scanning sections of breast lesions could cause misclassifications when only adopting one single Nakagami image. This study explored the feasibility of applying strain compounding technique to Nakagami image for considering multiple Nakagami images to reduce the diagnostic errors and improving the clinical performance in breast tumors classification. Strain compounding technique has been investigated to be useful for speckle reduction in conventional B-scan images due to scatterers redistribution caused by external compression. The external compression could cause tissue deformation which is relative to tissue stiffness and scatterers concentrations inside the breast tissue. Therefore, the continuous Nakagami images obtained by external compression could reveal the changes of scatterers concentration according to the stiffness of breast tissue and thus could be averaged to construct the strain-compounding Nakagami image. The difference of scatterers distribution between the original Nakagami image and the strain-compounding Nakagami image may be useful in distinguishing softer tissues from harder ones. In general, malignant breast tumor is harder than benign one due to desmoplastic reactions around surrounding tissues. Therefore, we propose the strain-compounding Nakagami imaging to classify breast tumors. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance. The area under the ROC curve was 0.97±0.02, the diagnostic accuracy was 96%, the sensitivity was 100%, and the specificity was 92%.
In conclusion, the strain-compounding Nakagami method shows that the different degree of hardness of breast tumors can be distinguished by the changes in Nakagami parameters. Therefore, the proposed method has the potential to be a useful computer-aided diagnosis tool in the detection and characterization of the breast masses.

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