面向B超图像分割的动态权重因子水平集方法

目的研究和改进水平集方法，实现B超图像中病灶区域的准确快速分割。方法分析已有水平集方法在B超图像处理中
的局限，基于区域水平集的优点，将信息论中的熵引入图像处理，定义动态权重因子，准确反映局部灰度阶梯变化状况，定量度
量轮廓线像素点分别受到趋向目标、背景区域的两种作用力的动态权重，将其融合到区域水平集中，迭代引导曲线形变和位
移。由于B超图像病灶分割属于指定区域的局部分割，所以将计算约束到局部范围，从而明显降低运算代价。结果动态权重
因子水平集方法能够较好分割B超图像中的病灶区域，与几种主流水平集方法相比，本文方法精度更高，时间复杂度更小。结
论动态权重因子方法能够更合理准确地判断病灶边界像素点，局部计算策略有效地提高了分割效率。

Level set method reconciled with a dynamic weighting factor for B-mode ultrasound image segmentation

Objective To modify the level set method for precise and fast segmentation of B-type ultrasound image lesions.
Methods Based on the best of region level set method, entropy in the information theory was introduced into image
processing to define a dynamic weighting factor that responded to the gradient change of the local gray levels to evaluate the
dynamic degree of driven force on each pixel on the contour to the target and background areas. The dynamic weighting
factors were reconciled into the regional level set model and led the contour to deform and move during the iterations. As
lesion segmentation was classified as local segmentation of a specific area, the calculation was restrained to the local sphere
abide by the contour, which greatly reduced the calculation complex. Results Experiments on B-type ultrasound images
showed improved results of the proposed method with a better accuracy and less time consumption compared with several
current level set methods. Conclusion Level set method reconciled with dynamic weighting factor allows a better evaluation of
the lesion contour pixels, and the local calculation strategy results in an enhanced segmentation efficiency.