面向医学图像分割的直线截距直方图倒数交叉熵方法
Segmentation Method Based on Line Intercept Histogram Reciprocal Cross Entropy for Medical Image

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 medica l image segmentation and provide more fully effective basis for clinical diagnos is and adjunctive therapy, a medical image segmentation method based on line int ercept histogram reciprocal cross entropy is proposed. Firstly, the line interce pt 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 histo gram is derived, according to which, the medical image is segmented. A large num ber of experimental results show that, compared with other methods, including two-dimensional reciprocal entropy method based on niche chaos particle swarm optimization (NCPSO), two-dim ensional exponent gray entropy method based on decomposition, symmetric cross en tropy method based on two dimensional histogram oblique segmentation, two dimens ional Tsallis cross entropy method based on particle swarm optimization (PSO) an d so on, the proposed method has superior image segmentation performance. In its segmentation result, object region is complete and accurate, and the edge details a re 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.