乳癌的一項重要警訊即微鈣化群集的出現。然而，因為在乳房X光影像上微鈣化模糊的特性及較差的對比度，使得人工診斷耗時且費神。因此，電腦輔助診斷系統的使用得以協助專家診斷，做為參考依據。
在本論文中，微鈣化的診斷機制分為四個階段。首先，取得精細的紋理同時抑制雜訊。在第二階段中，利用碎形維度及標準差找出候選區域。將取出的候選區域連接以形成感興趣區域 (ROI)。隨後，用光密度轉換增強所有ROI的對比度。第三階段， Haralick的紋理特徵及周圍區域相關性(SRDM)這些紋理分析的方法分別被應用在灰階和光密度轉換後的ROI。最後，在訓練和測試時，使用四折的交叉驗證程序伴隨著逐步線性區別分析。訓練出的區別函數用以分辨微鈣化及正常區域。
利用成大醫院提供的81件乳房攝影案例做實驗。利用本篇論文所提出的方法，可以達到當靈敏度96.1%時平均每張影像的偽陽數為7.66個及敏感度92.8%時每張影像偽陽數3.09個。在ROC曲線下的面積則為0.983±0.004。實驗結果顯示出本論文提出之系統在靈敏度及偽陽數上取得平衡且可預期減少專家的負擔。

One of the significant early signs of breast cancer is the appearance of micro-calcification (MC) clusters. However, it is time-consuming and exhausting to diagnose mammograms manually due to fuzzy nature of MCs and poor contrast in images. Thus, computer aided diagnosis systems are brought to assist specialists in diagnosis as a second opinion.
The MC detection scheme proposed in the thesis is composed of four stages. Firstly, detailed texture is acquired along with noise suppression. Secondly, fractal dimension and standard deviation are both applied to find candidate regions. These candidates are then connected to obtain regions of interest (ROIs). Then optical density (OD) transformation is implemented to all ROIs to enhance contrast. Thirdly, some texture analysis methods such as Haralick’s texture features and surrounding region dependence method are employed to each ROI in two forms, one in gray-level and the other in OD. Eventually, a four-fold cross validation procedure with stepwise linear discriminant analysis is used for training and testing. The trained discriminant function is used to distinguish regions with MCs from normal ones.
An experiment is conducted with 81 cases from National Cheng Kung University Hospital. The sensitivity of the proposed system achieves 96.1% with average 7.66 false positives per image (FPs/I) and 92.8% with 3.09 FPs/I. The area under ROC curve is 0.983±0.004. The results show that the proposed system strikes a balance between sensitivity and false positives and is expected to help the radiologists reduce their workload.

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