Automatic tumor segmentation of breast ultra-sound images using a distance-regularized level-set evolution method with initial contour obtained by guided image filter, L0 gradient minimization smoothing pre-processing, and morphological features

• Main Authors: Hsieh, Hsun, 謝 洵
• Other Authors: Jong, Tai-Lang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/t6z6cs

碩士 === 國立清華大學 === 電機工程學系所 === 105 === Due to the speckle noise and low contrast in breast ultrasound images, it is hard to locate the contour of the tumor by using a single method. In this thesis, a new method for finding an initial contour is proposed, which can improve the result of DRLSE on the segmentation of BUS images. The new method focuses on improving the algorithm proposed by Tsai-Wen Niu, which is a way to search an initial contour based on the local minimum in the images. When the BUS images contain calcification, it is possible to fail in searching of initial contour through such algorithm, hence leading to a poor segmentation result when the initial contour is on the wrong place. Therefore, we acquire a bigger initial contour by using a series of image smoothing methods and binarization, which can eliminate the weak edges and adjust the contrast in BUS images. In addition, some images without local minimum can be successfully detected by using the proposed method. However, the pixel value in these images are similar. It might be hard to accurately separate the tumor region from non-tumor region by the difference of pixel values. These obstacles are conquered by calculating the difference of length and pixel value in the suspect region. The ranking outcome is improved by using the morphological features. After applying DRLSE, our initial contour can reach the tumor region more accurately. To evaluate the result of segmentation, it is compared with the outcome of DRLSE obtained from different initial contours proposed by Tsai-Wen Niu, expansion DRLSE method, and contraction DRLSE method using three evaluation metrics, including ME, RFAE and MHD. The experimental results indicate that the proposed method is basically better than the other methods. However, the initial contour might contain non-tumor region when the edge of the tumor’s boundary is too ambiguous; even so, the proposed method drastically reduce the number of DRLSE iteration and computation time. According to the experimental results, the proposed method has three advantages over the other methods. First, it sets the initial contour automatically which is more efficient than setting the initial contour manually. Second, the region of the initial contour is much bigger than those obtained by the other methods, which can reduce the computation time and the number of DRLSE iteration. Third, if the tumor boundary is distinct, the new initial contour can improve the segmentation result of DRLSE.