基于区域相似度的局部拟合活动轮廓模型

Local Fitting Active Contour Model Based on Regional Similarity

针对局部二值化拟合(LBF)模型不能分割纹理图像和收敛速度慢等问题,提出一种结合局部拟合与区域间相似度的活动轮廓模型。该模型在LBF模型中引入基于图像梯度信息的加速因子和基于相似度局部拟合的相似项,通过在能量泛函中加入区域间灰度概率分布的相似信息,抑制图像噪声和提高分割准确度,引入梯度信息,能够放大目标轮廓附近驱动力,提高图像分割速度。实验结果表明,与LBF模型相比,该模型对噪声有较强的鲁棒性,能够分割纹理和灰度不均匀图像,且具有较高的分割速率。

To overcome the disadvantage of Local Binary Fitting（LBF）model that it cannot segment texture image and has too slow convergence speed,an active contour model combining local fitting energy and regional similarity is presented.It introduces an acceleration factor based on image gradient information and a similar term based on local similarity fitting.Image noise can be inhibited and segmentation accuracy is improved by introducing similarity information of regional intensity distributions into the energy function.The speed of image segmentation can be improved by introducing gradient information to amplify the driving force nearby the objective contour.Compared with the LBF model,the presented model can be more rubust to noise,can segment texture image,and has a high rate of segmentation.