基于边缘约束和边缘吸引力场正则化的多目标轮廓提取算法

Multi-Target Extraction Algorithm Based on Edge Restriction and Attraction Field Regularization

拓扑自适应模型是一种有效的多目标轮廓提取算法．但在MR脑图像的应用中却遇到了许多问题。针对该模型所存在的问题．提出一种基于边缘约束和边缘吸引力场正则化的多目标轮廓提取算法。算法利用关于目标轮廓的位置信息和区域信息对切片图像的边缘吸引力场进行边缘约束．得到仅包含目标的边缘图．从而减少相邻结构的边缘对曲线收敛的影响。同时．对进行边缘约束之后得到的边缘吸引力场进行正则化处理．扩大轮廓的搜索范围．增强轮廓模型对凹边缘的搜索能力和对断裂边缘正确提取的能力。实验证明．该算法可以快速有效地在MR脑图像中提取目标轮廓。

The topologically-adaptable model is an effective method for the contour detection of multiple objects on an image. However, it meets many problems when we apply it to MR brain images, such as poor convergence to boundary concavities, resulting from the broken boundary, and miserable anti-noise ability. In this paper, we proposes a new algorithm, named multi-target extraction algorithm based on edge restriction and attraction field regularization, to overcome these shortcomings. This new algorithm uses prior knowledge about target to perform edge restriction to get the only edge of the object of interest and to regularize attraction field to enlarge attraction field. Results show that the new algorithm can extract the target contour quickly and accurately when we apply it in MR brain images.