基於Level-set方法的全自動乳房超音波影像腫瘤偵測與切割

Abstract

In recent years, the computer-aided diagnostic (CAD) is developed gradually providing second opinion for radiologists' diagnosis. The CAD system should produce well segmentation result of tumor to precisely evaluate the tumor size, and the tumor can be further classified into benign and malignant by the extracted feature of shape. Concerning the previous techniques of the semi-automatic segmentation (e.g., level-set), the seed usually needs to be manually initialized at the appropriate position for the better segmentation result. Besides, most of the segmentation systems consider the detection procedure as the preceding step, and the segmentation approach is subsequently employed on the located region of interest. In this study, a fully automatic system integrating the tumor detection and segmentation steps is proposed, and a set of representative seeds are computerized for the whole image segmentation based on the level-set method. Meanwhile, the novel multi-seed mechanism assists the level set segmentation in acquiring the satisfactory result. The proposed system consists of three phases. First, a mean-shift clustering method and the affinity approach are applied to generate and extract the most representative seeds. Next, the level-set method based on the selected seeds is employed to obtain several suspected regions. Finally, the features of all the suspected regions are calculated and further analyzed by support vector machine (SVM) classifier to extract the target tumor from the normal ones. 120 cases (68 for benign cases and 52 for malignant cases) are used for evaluating the proposed system. The sensitivity is 95% (114/120) with the benign cases equal to 0.91 and the malignant cases to 1.00. In summary, the experimental results demonstrate the high efficiency and robustness of the proposed method.