Abstract
Lesion segmentation for breast ultrasound images has been studied by many people, but it is hard to achieve using traditional edge detection because ultrasound images don’t have sharp contours and exist a lot of noise. In this thesis, we propose an automatic segmentation method for breast ultrasound images which combines morphological image processing and distance-regularized level-set evolution method (DRLSE), and improve combined DRSLE (cDRLSE) proposed by Yung-Hsuan Hsu. The most serious problem of cDRLSE is that it spend a lot of time on obtaining initial contour through the operation which applied the texture features (e.g., gray level co-occurrence matrix (GLCM)) for support vector machine (SVM), therefore we propose a new method based on morphological operations to search for initial contour which is effective and efficient. Using the new initial contours, we can capture the tumor area more precisely after applying DRLSE. To evaluate the result of segmentation, we compare it with expansion DRLSE method, contraction DRLSE method and cDRLSE method using three evaluation metrics, including misclassification error (ME), relative foreground area error (RFAE) and modified Hausdorff distance (MHD). We find that the proposed method is basically better than expansion DRLSE method and contraction DRLSE method which confirms the importance of initial contour to DRLSE. However, it is better than cDRLSE method in RFAE but worse than cDRLSE in ME and MHD, probably due to ranking failure in the proposed method or improved segmentation accuracy of post processing in cDRLSE method; even so, the proposed method not only spends less time obviously but also has no need to apply post processing. The proposed method has the following properties: 1.A fully automatic segmentation method for breast ultrasound images which has no need to set initial contour manually. 2.The way to obtain initial contour is efficient, and moreover if the tumor is smooth, the initial contour will be close to the tumor’s real boundaries. 3.Compared with other methods, the segmentation result of the proposed method is truly closer to ground truth image if the proposed initial contour mostly lies inside the ground truth.