主动轮廓模型及其在医学体分割中的应用

利用医学序列切片在灰度值和空间位置上的相关性,对传统主动轮廓模型进行改进,将其应用于医学体数据分割中.以物体轮廓采样到其相邻两采样点中点的距离为内部能量函数,同时通过制约轮廓的长度,使其尽可能短来达到;利用图像区域的局部梯度信息,同时利用序列图像之间局部区域的全局信息及其相关性重新构造外部能量函数;并根据内外部能量的比值,动态的调节权值参数.实验结果表明,改进算法既可以有效地检测出一些拐角点和凹点,又可以避免目标边缘收敛于某些噪声点或伪边缘点,可达到良好的体分割效果.

Active contour Model and Its Application in Medical images Volume segmentation

In order to overcome the instability and uncertainty of the volume segmentation of medical images via the conventional active contour model, this paper proposes an improved active contour model based on the pertinence of gray scale and space position between medical series images. In the proposed model, an internal energy function is constructed according to the distance between the left neighbor midpoint and the right adjoining midpoint, which is limited by restricting the contour length. Then, an external energy function is constructed according to the local gra- dient information of image as well as the overall information and its pertinence in the local region between series images. The weight parameters are automatically regulated according to the ratio of internal energy to external energy. Experimental results show that the proposed algorithm can detect some corner and concave points and can prevent the object edge from converging to noise and pseudo edge points, thus resulting in good volume segmentation effect.