乳房動態對比增強及擴散加權磁振影像的電腦輔助診斷

Abstract

乳癌是女性在世界中最常罹患的癌症，而也是女性的主要死因之一。如果能早期發現有類似的症狀發生，就有較高的機會能夠完全治癒。近年來，電腦的輔助診斷不只提供了腫瘤大小、位置的資訊，甚至能進一步的分辨出腫瘤的良惡性。在這篇論文中，動態對比增強的核磁共振影像用來紀錄顯影劑隨著時間的變化所成的像，擴散加權磁振影像則提供了水分子在不同區域組織中的擴散速率。動態對比影像中腫瘤的切割是由一位有經驗的放射科醫生來操作，擴散加權磁振影像中腫瘤的切割則是先經過影像套合處理後，加上相對應動態對比影像中切出的腫瘤區域合併後擷取出。在分析腫瘤良惡性的部分，我們利用改良的fuzzy C-means方式找出能代表腫瘤的動力曲線，接著使用Tofts動力曲線模型來分析，其模型所得的參數為其動力曲線之特徵。此外，三維型態分析包涵了形狀及材質的特徵，而形狀分析有緊實度、邊緣的變化以及與所對應的橢圓模型比對的結果作為三維形狀上的特徵。材質的特徵使用了灰階值共生矩陣對腫瘤進行紋理的分析。另外，由擴散加權磁振影像中所得的特徵提供一個水分子在腫瘤中擴散速率的量化分析並描述腫瘤內組織的不均勻程度。上述所提出的多種特徵為判斷腫瘤良惡性的依據，提供作為實驗統計上的資料。在這個實驗中，總計138個病理檢驗過的腫瘤作為實驗資料，其中包涵54個良性、84個惡性的病例。最後結果能達到準確性91.30％ (126/138)、敏感性92.86％ (78/84)、專一性88.89％ (48/54)及Az值0.9333。

The breast cancer is the most common cancer of women in the world and it is the major cause of the death for women in recent years. However, it is a type of cancer which has an excellent curability if it can be detected in early stage. Recently the computer-aided diagnosis systems can provide radiologists not only the existence information of the tumors but also the lesion classification of malignancy and benignancy. In this paper, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to detect changes of the contrast agent (CA) concentration over time which are modeled as a result of the exchange of CA molecules between compartments. Furthermore, the diffusion-weighted magnetic resonance imaging (DWI) is used to provide the information about local characteristic of water diffusion of biological tissues. An experienced radiologist indicates the tumor in DCE-MRI and a region growing based algorithm is then applied to segment the tumor. After the manual registration process between DCE-MRI and DWI by a radiologist, the tumor region in corresponding DCE-MRI is mapped into DWI. Then a modified fuzzy c-means clustering is used to identify the most possible malignant kinetic curve of the segmented tumor for analysis. The Tofts pharmacokinetic model is used to fit the kinetic curve of the tumor and the parameters of the model are used as the diagnosis features. Furthermore, the three-dimensional (3-D) morphology features, shape and texture, from DCE-MRI are proposed to improve the diagnosis performance. The shape features including compactness, margin, and ellipsoid fitting could describe the 3-D shape information of the tumor and the 3-D texture features based on the grey level co-occurrence matrix is also used to analyze the lesions. Besides, the apparent diffusion coefficient (ADC) features extracted from DWI give the quantitative measurement for the water diffusion of a lesion are also included in this study. In the experiments, 138 biopsy-proved lesions with 54 benign and 84 malignant used to evaluate the performance of the proposed diagnosis system for breast MRI. Its accuracy, sensitivity, specificity, and Az value are up to 91.30% (126/138), 92.86% (78/84), 88.89% (48/54), and 0.9333, respectively. From the experiment result, the conventional kinetic characteristics features, Tofts features and ADC features could have the better performance. Especially, the ADC features have the best sensitivity.