肝癌及肝硬化是兩種很常見的肝臟疾病，針對此兩類肝臟疾病而言，早期發現及早期治療可以提升治療的效果，而肝臟超音波影像即是用以執行例行性肝臟檢查、以利早期診斷最常用的工具。然而目前之超音波肝臟組織辨識主要仰賴醫師辨認影像中特定的紋理特徵，透過電腦視覺系統的分析，我們希望輔助醫生做出更精確的診斷。而在實現此一系統時，影像的特徵擷取是相當重要的一環；在超音波肝臟影像的辨識應用中，已有多種的特徵擷取法被提出，其中包含了以M-頻段小波轉換為基礎的碎形特徵向量及能量、以Gabor濾波器為基礎的碎形特徵向量及能量、共生矩陣紋理分析法（Spatial Grey Level Dependence Matrix，SGLDM）等。

本論文主要探討的問題是：是否可以透過結合不同的特徵向量，並經由適當的特徵選取機制，選出重要的特徵提升辨識的準確性？我們提出了一個利用演化法為基礎的階層式特徵融合法，可以透過一個階層式的架構，從多個特徵向量中，選出具有鑑別度的特徵來執行肝臟超音波織織辨識。首先，我們利用不用的特徵擷取法，取出多個特徵向量。在系統的第一層，演化法為基礎的特徵選取法選出個別特徵向量的特徵子集；接著多個特徵子集透過特徵融合法合併成融合後之特徵子集。最後，再執行提出之特徵選取法選出最後用以辨別之特徵。

經由實驗顯示，利用此一系統所選出的融合特徵子集相較於個別特徵向量所選取之特徵子集，可以達到更高的辨識正確率，也驗證了此一系統可以從多個特徵向量中，找出具鑑別度的特徵，用以辦別正常肝、肝硬化、肝癌等三類的超音波肝臟影像。

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