Tumor Detection for Automated Whole Breast Ultrasound Image

• Main Authors: Wei-Wen Hsu, 徐位文
• Other Authors: 張瑞峰
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/06884134067348403735

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 99 === 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.