全乳房自動超音波影像之腫瘤偵測

Abstract

乳癌近幾年來一直是女性癌症中的主要死因。然而，藉由早期的檢測及治療能大幅地降低乳癌的死亡率。在許多乳房檢測儀器中，乳房超音波與乳房X光攝影的結合能在檢查乳癌上有著互補的作用。近幾年來，自動的全乳房超音波已經有不少的論文研究，其在腫瘤的方位顯示和記錄上都有很好的表現。因為一個病例會有很龐大的三維影像資料，逐張檢查診斷會花費醫師許多時間。因此，此論文提出一個電腦輔助的乳房腫瘤偵測系統偵測腫瘤可疑的區域，以協助醫生做診斷。本論文採用的腫瘤偵測系統是以區域為基礎做運算處理。首先，fast 3-D mean shift方法將3維影像資料分割成區域並移除影像中的雜訊。接著，fuzzy c-means clustering會將這些區域依其灰階值來做分群。因為在超音波影像中，腫瘤一般會有較低的灰階值，所以這些被分到最暗的區域會被視為是腫瘤的可疑區域。之 後，在這些可疑的區域中，如果灰階值相差在門檻值內的區域會結合在一起呈現最後切割的結果。不僅如此，為了更進一步去區分腫瘤和非腫瘤，每個切割後腫瘤的可疑區域會取出七個特徵，結合這七個特徵去做分類，以減少非腫瘤被誤判為腫瘤情況的發生。在這個實驗中，大部份的腫瘤都能被找到。在平均每個病例有4.92個非腫瘤被誤判為腫瘤的情況下，系統的腫瘤偵測準確率是89.04%(130/146)，其中惡性腫瘤的偵測準確率更可達到94.03%(63/67)。希望在醫師診斷的輔助上能有所幫助。

Breast cancer has been the major cause of death for women among all kinds of cancers in recent years. Nonetheless, the early detection and improved treatment can significantly reduce the mortality of breast cancer. Breast ultrasound (US) is a very important complementary imaging modality with mammography in breast cancer detection. Recently, the automatic whole breast ultrasound (ABUS) system has been developed to provide the proper orientation and documentation of breast lesions.Because a large three dimension (3-D) volume image is obtained for each case, the physician needs to spend a lot of time in reviewing all slice images. Therefore, a computer-aided tumor detection system is proposed to find the suspicious regions of tumors and assist the physician in diagnosis. The region-based ABUS tumor detection method is adopted in this study. At first, the 3-D volume image is segmented into regions and the speckle noise is removed by the fast 3-D mean shift method. Subsequently, the fuzzy c-means(FCM) clustering classifies these regions into different classes according to their intensities. Because tumors are usually darker than normal tissues in US, the regions classified into the darkest cluster by the FCM are regarded as the suspicious tumor regions in this study. After FCM, these suspicious regions are merged within a merging threshold to present the segmented results. Moreover, in order to discriminate the real tumors from the other non-tumor regions, seven features are extracted from the suspicious tumor regions and the classification method is adopted with 10-fold validation to reduce the false-positives. By experimental results, almost all the tumors can be found by this system and the sensitivity is 89.04% (130/146) with 4.92 FPs per case. Furthermore, the detection rate for malignant tumors is up to 94.03% (63/67). The proposed tumor detection system is useful for the diagnosis of doctors.