摘要
为了进一步提高医学图像分割的速度和准确度,为临床诊断和辅助治疗提供更为充分有效的依据,本文提出了一种基于直线截距直方图的倒数交叉熵图像阈值分割方法。首先定义了直线截距直方图;然后根据医学图像的二维信息,建立该图像的直线截距直方图;最后,推导出基于该直方图的倒数交叉熵阈值选取准则,并以此对医学图像进行分割。实验结果表明,与基于混沌小生境粒子群优化(Niche chaotic mutation particle swarm optimization,NCPSO)的二维倒数熵法、基于分解的二维指数灰度熵法、基于斜分的二维对称交叉熵法及基于粒子群优化(Particle swarm optimization,PSO)的二维Tsallis交叉熵法相比,本文方法分割后的图像中目标区域完整准确,边缘细节清晰丰富,且所需运行时间大幅减少,是医学影像研究中可选择的一种快速有效的图像分割方法。
To improve the efficiency and accuracy of medical image segmentation and provide more fully effective basis for clinical diagnosis and adjunctive therapy,a medical image segmentation method based on line intercept histogram reciprocal cross entropy is proposed.Firstly,the line intercept histogram is defined.Then,the line intercept histogram of the medical image is built considering its two-dimensional information.Finally,the reciprocal cross entropy criterion for threshold selection based on the line intercept histogram is derived,according to which,the medical image is segmented.A large number of experimental results show that,compared with other methods,including two-dimensional reciprocal entropy method based on niche chaos particle swarm optimization（NCPSO）,two-dimensional exponent gray entropy method based on decomposition,symmetric cross entropy method based on two-dimensional histogram oblique segmentation,two-dimensional Tsallis cross entropy method based on particle swarm optimization（PSO）and so on,the proposed method has superior image segmentation performance.In its segmentation result,object region is complete and accurate,and the edge details are clear and richer.Moreover,the running time is greatly reduced.It is a fast and effective new segmentation method which can be used in medical image research.
出处
《数据采集与处理》
CSCD
北大核心
2015年第5期982-992,共11页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(60872065)资助项目
江苏省普通高校研究生科研创新计划(SJLX15_0116)资助项目
江苏高校优势学科建设工程(2012)资助项目
中央高校基本科研业务费专项资金资助项目
关键词
医学图像分割
阈值选取
直线截距直方图
倒数交叉熵
medical image segmentation
threshold selection
line intercept histogram
reciprocal cross entropy
