Image segmentation algorithm based on the improved watershed algorithm

被引：0

作者：

Sun, Huijie ^{[1
,2
,4
]}

Deng, Tingquan ^{[1
,3
]}

Li, Yanchao ^{[3
]}

机构：

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin

[2] College of Computer Science and Information Engineering, Harbin Normal University, Harbin

[3] College of Science, Harbin Engineering University, Harbin

[4] The Heilongjiang Provincial Key Laboratory of Intelligence Education and Information Engineering, Harbin Normal University, Harbin

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2014年 / 35卷 / 07期

关键词：

Image segmentation; Mathematical morphology; Over-segmentation; Particle swarm optimization; Region growing; Shannon entropy; Watershed algorithm;

D O I：

10.3969/j.issn.1006-7043.201309067

中图分类号：

学科分类号：

摘要：

An improved watershed image segmentation algorithm based on particle swarm and region growing was proposed to solve the problems of noisesensitivity and over-segmentation. The improved algorithm, combining region growing with the classical watershed algorithm, was established by constructing an objective function based on Shannon entropy to determine the parameter of the region growing. The regional disparity degree was calculated by the gray mean, and the smaller region was merged with the neighbor region with a minimal disparity degree. The particle swarm optimization algorithm was employed to search the global optimization of the objective function. Experimental results show that this improved algorithm is better than other image segmentation methods, and can solve effectively the problem of over-segmentation that existed with the watershed algorithm. The segmentation results conform to the visual characteristics of the human eye, so this algorithm is therefore an effective, accurate, and practical image segmentation method.

引用

页码：857 / 864

页数：7

共 18 条

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