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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40449
標題: | 全乳房超音波影像之三維模糊理論腫瘤偵測 3-D Fuzzy Tumor Detection for Whole Breast Ultrasound Image |
作者: | Tzu-Hsuan Chen 陳子軒 |
指導教授: | 張瑞峰(Ruey-Feng Chang) |
關鍵字: | 超音波,模糊理論,乳房,三維, ultrasound,fuzzy,breast,3-D, |
出版年 : | 2008 |
學位: | 碩士 |
摘要: | 乳癌是女生最常發生的癌症之一,且致死率排名第二位,僅次於肺癌。早期檢測及進步的治療能有效地減少乳癌造成的死亡。乳房超音波是一個相當重要的影像檢查方法,與乳房X光攝影互補,在早期檢測乳癌是否發生。然而,手動的超音波需要花許多醫師的時間來掃瞄病人。最近,自動的全乳房超音波被發展來掃瞄乳房,以節省醫師的時間。因為一個病例可掃出許多影像,醫師仍需花費許多時間來診斷。因此,此論文提出一個電腦輔助的腫瘤偵測系統來檢測可疑的腫瘤區域,以節省診斷的時間。在這個系統裡,影像首先作前處理: 移除左右兩側黑邊及降低解析度可以減少偵測時間; S型濾波器可以強化腫瘤與正常組織的邊界。之後,三維模糊理論被用來偵測在乳房裡的腫瘤。這個理論根據影像的灰階強度及邊緣的資訊,將影像分成三類: 分別為腫瘤、邊界及正常組織這三種區域。因為並非全部被模糊理論偵測出來的腫瘤區域都是真實的,某些可能是像陰影或乳頭之類的較暗區域。為了獲得真正的腫瘤區域,先用連通單元標示演算法將每一個腫瘤裡的像素各別地群聚成連通單元,再根據這些連通單元的特性,使用一些腫瘤判斷標準來濾掉非腫瘤的部份。在這個實驗中,有45個病例被偵測系統測試,根據實驗結果,幾乎所有的腫瘤能夠被此系統發現,且敏感度高達85.9% (55/64)而平均每一個病例僅有1.71個錯誤。這說明了這個系統滿足了高的偵測效能,而只有少量的錯誤比例,以及是個對醫師診斷有所幫助的有用工具。 The breast cancer is the most frequently cancer in women and it is the second rank for the death caused by cancer after cancer of lung. The earlier detection and improved treatment are effective to reduce deaths due to breast cancer. Breast ultrasound is a quite important complementary imaging modality with mammography to detect breast cancer early. However, it needs a lot of physician time to screen a patient by the manual ultrasound. Recently, the automatic whole breast ultrasound has been developed to save the physician for screening the breast. Because a lot of images are obtained for a case, the physician still takes a lot of time to diagnosis. Hence, a computer-aided tumor detection system is proposed to find suspicious regions of tumors for saving the diagnosis time. In this system, the image is firstly pre-processed by removing black regions and sub-sampling to reduce the detecting time and the sigmoid filter to enhance the boundary between tumor and normal tissue. Then, a three-dimensional (3-D) fuzzy technique is adopted to detect tumor regions in the breast. This method classifies the image as three categories, tumor, boundary, and normal tissue according to the intensity and edge information of image. The detected tumor regions are not all real tumor regions. Some of them may be darker regions, such like shadow or nipple. In order to obtain actual tumor regions, the connected component labeling groups each voxel of tumor regions individually and some tumor criteria are proposed to filter out non-tumor regions according to the characteristic of these connected components. In the experiment, 45 test cases are tested by the proposed tumor detection system. By experimental results, almost all tumors can be found by this system and the sensitivity is up to 85.9% (55/64) with 1.71 false-positive rate per case. This means that the proposed system satisfies the high detecting performance with low false-positive rate and is a good tool to help the diagnosis of doctors. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40449 |
全文授權: | 有償授權 |
顯示於系所單位: | 資訊工程學系 |
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