基于曲线自适应的肝脏病灶CT批量分割算法

Liver lesion CT volume segmentation algorithm based on self-adaptive curve

提出了一种肝脏病灶的快速分割方法。为了适用于医学图像的批量处理,首先给出一种基于区域的映射方法预提取初始区域作为曲线演化的初始条件。为了减少伪边界的影响,并使轮廓线充分收敛至凹陷区域,提出了一种基于曲线自适应的改进G-S模型对病灶进行精准拟合。该方法与传统的分割方法相比,既大大提高了精准度,又无需消耗巨大的运算量,同时还去除了图像噪声的干扰。实验结果表明,该方法能够有效地提取病灶轮廓,满足临床中真实感的需求。

Aiming at abdomen CT images of different patients, the fast segmentation algorithm for liver lesions is presented. Firstly, by using contour map and region algorithm in combination, the initial contour of curve evolution is pre-extracted, which is suitable for the batch. In order to reduce the impact of spurious boundary and to converge to deeper boundary concaves, the improved G-S model based on adaptive curve can be finally presented to accurately fit the contour. Compared with other classical segmentation algorithms, not only is this method used to improve the precision of active contour model, it also proves to save considerable time. At the same time, noise jamming can be removed. Experiments show that the proposed methods could effectively extract the lesion region of image and meet the needs of reality.