| Summary: | 碩士 === 臺灣大學 === 資訊工程學研究所 === 98 === Breast cancer has topped the first spot of women’s cancer occurrence in recent years. The early detection and improved treatment are significant to reduce death of breast cancer. Breast ultrasound is a very important complementary imaging modality with mammography to detect breast cancer. However, the physician needs a lot of time to screen a patient by the manual ultrasound. Recently, the automatic whole breast ultrasound system has been developed to reduce the physician’s time for screening the breast. Because a large number of images are obtained for a case, the physician will still take a lot of time to diagnosis. Therefore, a computer-aided tumor detection system is proposed to find suspicious regions of tumors for reducing the diagnosis time. In the tumor detection system, the images are firstly pre-processed by removing black regions and sub-sampling to reduce the detecting time, then the anisotropic diffusion is applied to smooth the images, and the sigmoid filter is applied to enhance the contrast between tumor and normal tissue. In the detection stage, the fuzzy c-means clustering classifies all the pixels into several groups with similar grey intensities. The connected components labeling is used to find all the connected areas in the 3D ABUS image based on the results of the fuzzy c-means clustering. The tumor criteria are proposed to reduce the number of suspicious regions and only the high potential tumor regions could be remained after applying the criteria. In this experiment, there are 130 cases and they were separated into two groups, the 55 cases are used as the training cases and the remaining 75 cases are used as for testing. By experimental results, almost all tumors can be found by this system and the sensitivity is up to 84.1% (79/94) with 2.37 FP rate per case. The proposed tumor detection system is a good tool to help the diagnosis of doctors.
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