A new image segmentation technique using bi-entropy function minimization
A new image segmentation technique using bi-entropy function minimization
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DOI：
10.1007/s11042-017-5429-8
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发表时间：
2017-12
影响因子：
3.6
通讯作者：
K. Chowdhury;D. Chaudhuri;Arup Kumar Pal
中科院分区：
文献类型：
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作者：
K. Chowdhury;D. Chaudhuri;Arup Kumar Pal
图像分割，即根据感兴趣区域（ROI）将多光谱和全色图像分割成同质像素组，是许多高级图像处理和目标识别的通用步骤。由于图像分割的质量至关重要，因此它本质上影响着任何自动图像分析系统的整体性能。图像分割可以通过递归地分割整个图像或将大量微小区域合并在一起直到满足指定条件来执行。阈值分割是灰度图像分割中一种古老、简单而又重要的方法。本文利用香农熵，提出了一种新的基于双熵函数最小化的多级阈值图像分割方法。引入了一种基于aw×wmoving窗口内像素权值的平滑技术，使分割结果连续且定性。该算法充分考虑了空间信息和灰度信息，减少了计算量。实验对标准医学图像、纹理图像和遥感图像进行了分割，并与其他相关的分割方法进行了对比。实验结果表明，该方法收敛速度快，计算效率高。
Image segmentation, the splitting of a multispectral and panchromatic image into groups of homogeneous pixels based on the region of interest(ROI), is a universal step for many advanced image processing and object recognition. Image segmentation essentially affects the overall performance of any automated image analysis system due to utmost importance of its quality. Image segmentation can be performed by recursively splitting the whole image or by merging together a large number of minute regions until a specified condition is satisfied. Thresholding is an old, simple and important method in gray scale image segmentation. In this paper, we have used Shannon’s entropy and proposed a new multilevel thresholding image segmentation method based on minimization of bi-entropy function. A smoothing technique based on weight value of the pixel within aw×wmoving window is introduced to make the splitting result continuous and qualitative. The proposed algorithm takes full account of the spatial information and the gray information to decrease the computing quantity. Standard medical images, texture images, and remote sensing images are segmented in the experiment and compared with other related segmentation methods with different measures. Experimental results show that the proposed method can quickly converge with high computational efficiency.