基于信息熵的自适应尺度活动轮廓图像分割模型

摘要

In view of the problem that the fixed scale active contour model cannot quickly and accurately segment images with intensity inhomogeneity, an adaptive scale active contour model based on the information entropy is proposed for image segmentation.Firstly, we put forward a novel energy function by using Maximum Posterior Probability (MAP) and Bayes classification criterion, which greatly improve the ability to extract the image intensity information and the segmentation accuracy for inhomogeneous images.Secondly, we construct an adaptive scale operator by using the image information entropy to let the model can automatically adjust the scale according to the intensity inhomogeneity degree of the image, which improves the segmentation speed of the model.Finally, in order to verify the superiority of our model, we make a comparison between our model and LGDF model, and also make an objective and quantitative analysis of the segmentation results by using the segmentation time, the number of iterations and the similarity index.The final results show that the proposed model not only has high robustness to the initial contour, but also has high accuracy and efficiency in segmenting images with intensity inhomogeneity.%针对固定尺度活动轮廓模型无法快速准确分割灰度不均匀图像的问题,提出了一种基于信息熵的自适应尺度活动轮廓图像分割模型.首先,利用最大后验概率(MAP)以及贝叶斯分类准则,提出了一种新型能量泛函,提高了模型对灰度信息的提取能力,进而极大提高了模型对灰度不均匀图像的分割准确度.其次,利用图像信息熵构造了自适应尺度算子,使模型能根据图像灰度不均程度自动调整尺度,提高了模型对灰度不均匀图像的分割速度.最后,为验证文中模型的优越性,将该模型与LGDF模型进行了对比,并通过分割时间、迭代次数以及相似度等指标,对分割结果进行了客观、定量分析.最终结果表明,该模型不但对初始轮廓具有较高鲁棒性,而且对灰度不均匀图像具有较高的分割准确性与分割效率.