基于信息测度的图像过渡区提取与分割  

Image transition region extraction and segmentation based on information measure

作　　者：康文炜[1] 康文颖[2] 康晓涛[1] 

机构地区：[1]吉林大学通信工程学院,长春130022 [2]吉林大学第二医院心血管内科,长春130041

出　　处：《吉林大学学报（工学版）》2013年第S1期414-418,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金资助项目(50977037);吉林大学科学前沿与交叉学科创新项目(201103213)

摘　　要：图像过渡区提取的传统算法基于梯度算子,为克服梯度算法对噪声敏感的缺点,通过对图像过渡区特征的深入分析,提出基于信息测度的过渡区直接提取方法,依据过渡区的直方图确定一个最佳分割阈值。根据过渡区像素灰度变化频繁的特点,构造提取图像过渡区的特征参数———局部熵信息测度。实验结果表明,算法抗噪性能好,稳健性强,摆脱了传统算法对剪切点Llow和Lhigh的依赖,优于传统的过渡区间接提取算法和基于局部复杂度的过渡区直接提取方法。Traditional transition region extraction methods are based on gradient operator.The conventional gradient-based methods are sensitive to noise.According to the characteristics of transition region,the segmentation method based on information measure was presented.Then optimal segmentation threshold was attained according to the transition region histogram.The transition region was obtained by local entropy information measurement.The proposed method depend no more on Llow and Lhigh.The filtering ability of local entropy information measurement improves the ability of proposed method to deal with noises.Experimental results indicate that the proposed method outperforms the other methods.

关 键 词：图像分割 过渡区提取 梯度 信息测度 阈值 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]