基于多尺度局部特征的图像分割模型

摘要

In order to address the issue of gray scale inhomogeneity and initial contour sensitivity,an image segmentation model based on multi-scale local feature is proposed.Different from traditional local neighborhood defined in square shape region,the circular shape is used to capture more local information in the model.Taking into account intensity varies in different levels in local region,the method combines multi-scale structure with mean value filter is proposed to acquire multi-scale local grayscale information.An approximation of true image,which is obtained by transforming grayscale inhomogeneity model,is incorporated into the local Gaussian distribution fitting (LGDF) model and the energy function is constructed with multi-scale local feature.The theoretical analysis and experimental results demonstrate that the proposed method can rapidly and accurately segment grayscale inhomogeneity image,and also has strong robustness to the initial contour since multi-scale structure weakens the influence of intensity inhomogeneity.%为了解决图像分割中灰度不均匀和初始轮廓敏感的问题,提出一种基于多尺度局部特征的图像分割模型.与传统局部邻域定义在方形区域不同,该模型采用圆形区域来获取更多的局部信息;考虑到局部区域灰度的变化程度不一,提出利用多尺度结构与均值滤波器相结合的方法获得多尺度局部灰度信息;通过转换灰度不均匀模型得到一个逼近真实信息的图像,并将其融合进局部高斯分布拟合(LGDF)模型,构造出基于多尺度局部特征的能量泛函.从理论分析和实验结果表明:由于多尺度结构弱化了灰度不均匀的影响,该模型既能快速、准确地分割灰度不均匀图像,又表现出对初始轮廓具有较强的鲁棒性.