基于模拟退火的简化Snake弱边界医学图像分割

弱边界医学图像的分割一直是图像分割技术中的一个难点，为了有效地对弱边界医学图像进行分割，提出了一种简化的Snake图像分割算法，该算法对传统Snake模型进行了改进，即运用简化Snake的思想，特别是在内能表达式中添加了系数可变的面积项，并且引入了模拟退火算法与已改进的简化Snake模型相结合的方法，使得图像的分割效果有了较好的改进。另外，还讨论了模拟退火算法中邻域的选取、随机变量的产生机制以及接受准则等对搜索到理想的最优解所起的作用。该算法运用到医学图像分析中的实验证明，该算法对弱边界信息图像的分割能取得较好的效果，而且运算的时间复杂度低。

Simulated Annealing Based Simplified Snakes for Weak Edge Medical Image Segmentation

Segmentation on weak edged medical image is a difficulty in segmenting technology. In this paper, a simplified snake algorithm for image segmentation is proposed. This proposed model introduces the idea of simplified snake to improve the traditional snake model especially by adding an area energy term with variable coefficients into the internal energy term. This area energy term does well in improving the initialization problem, furthermore, it keeps the low time complexity of original simplified model. And besides, this paper also introduces simulated annealing algorithm to this improved simplified snake model and this algorithm makes a better effects on image segmentation. In this paper, the author discusses the choice of adjacent region, mechanism of generating random variables and the acceptance principles, etc. which are all playing an important role in searching the ideal optimum solution in simulated annealing algorithm. This simulated annealing based simplified snake model proposed in the paper has been tested on medical images. Enough experiments and the results comparing with traditional snake have proved that this proposed algorithm shows a significant improvement in segmenting weak edged medical images with a low time complexity.