用Normalized Cut法自动提取乳腺超声图像中的肿瘤边缘

Automatic Boundary Extraction of Breast Tumor Lesions in Ultrasound Images Using Normalized Cut Algorithm

提出一种带权重邻域灰度信息的normalized cut(Ncut)方法,该方法能够全自动提取乳腺超声图像的肿瘤边缘.通过Ncut分块乳腺超声图像中的各块灰度及空间分布特征来识别待检测肿瘤的轮廓.对于少数分割不精确的结果,可用结合局部能量项的动态轮廓模型对所提取的初始边缘进行修正,使其更接近真实目标轮廓.对包含112幅乳腺肿瘤超声图像的数据库进行边缘提取,结果表明:该方法无需人工干预,能够准确有效地实现肿瘤分割,且计算量小,有望提高计算机辅助诊断的自动化程度.

A modified normalized cut（Ncut） method considering the weighted gray values of neighborhood pixels is proposed to automatically segment the breast tumor lesion in ultrasonic images.The method partitions a breast ultrasound image into clusters with Ncut,and uses different gray values and the spatial distribution of each cluster to obtain an initial contour of the breast tumor.Then,for a small percentage of inaccurate segmentation,an active contour model together with a region-scalable fitting term is used to adjust the initial boundary for the final result.The proposed method is applied to a database of 112 clinical ultrasonic breast tumor images.The results show that the proposed method can realize boundary extraction of tumors efficiently and automatically without any manual intervene.Meanwhile the computation complexity is low.Therefore, the method can be used to improve the degree of automation in computer-aided diagnosis.