Improved order-invariant watershed algorithm for image segmentation

• Graphics and image processing • Previous Articles Next Articles

Improved order-invariant watershed algorithm for image segmentation

Received:2007-11-01 Revised:1900-01-01 Online:2008-04-01 Published:2008-04-01

一种顺序无关的改进分水岭图像分割算法

张鲲王士同

江南大学信息工程学院江南大学

通讯作者: 张鲲

Abstract

Abstract: Creative lake minimum was introduced to represent topographical information of a pixel, so as to reduce the number of RIDGE labels in orderinvariant watershed algorithm. Watershed fall was used to reduce the number of over-segmented regions. This algorithm was implemented in both immersion and toboggan ways. Experiment results show that the proposed algorithm is still order-invariant, and it can reduce the number of RIDGE labels by about 80% as well as segmented regions by 5%~20%.

Key words: Image segmentation, watershed algorithm, immersion method, toboggan method.

摘要： 为了减少顺序无关分水岭算法中的脊线标记RIDGE的数量，引入像素的湖最小值作为附加地形特征来消除不确定性。同时为了解决分水岭算法的过分割问题，引入落差来控制分割区域的形成过程。实验结果表明，改进算法在增加有限计算复杂度的情况下，将RIDGE标记数量减少了约80%，改善了原顺序无关算法中不确定像素过多的问题，将分割结果区域的数量减少了5%~20%，并且算法保持了顺序无关的特性。

关键词: 图像分割, 分水岭算法, 泛洪算法, 降水算法

Shi-Tong WANG. Improved order-invariant watershed algorithm for image segmentation[J]. Journal of Computer Applications.

张鲲王士同. 一种顺序无关的改进分水岭图像分割算法[J]. 计算机应用.

[1]	Guangzhu XU, Wenjie LIN, Sha CHEN, Wan KUANG, Bangjun LEI, Jun ZHOU. Fundus vessel segmentation method based on U-Net and pulse coupled neural network with adaptive threshold [J]. Journal of Computer Applications, 2022, 42(3): 825-832.
[2]	YANG Rui, QIAN Xiaojun, SUN Zhenqiang, XU Zhen. Hybrid aerial image segmentation algorithm based on multi-region feature fusion for natural scene [J]. Journal of Computer Applications, 2021, 41(8): 2445-2452.
[3]	CAO Yuhong, XU Hai, LIU Sun'ao, WANG Zixiao, LI Hongliang. Review of deep learning-based medical image segmentation [J]. Journal of Computer Applications, 2021, 41(8): 2273-2287.
[4]	HUANG Li, LU Long. Segmentation of ischemic stroke lesion based on long-distance dependency encoding and deep residual U-Net [J]. Journal of Computer Applications, 2021, 41(6): 1820-1827.
[5]	SHEN Xuewen, WANG Xiaodong, YAO Yu. Spatial frequency divided attention network for ultrasound image segmentation [J]. Journal of Computer Applications, 2021, 41(6): 1828-1835.
[6]	LUO Qin, WANG Yan. Image segmentation model without initial contour [J]. Journal of Computer Applications, 2021, 41(4): 1179-1183.
[7]	DONG Yang, PAN Haiwei, CUI Qianna, BIAN Xiaofei, TENG Teng, WANG Bangju. Few-shot segmentation method for multi-modal magnetic resonance images of brain tumor [J]. Journal of Computer Applications, 2021, 41(4): 1049-1054.
[8]	HU Yishan, QIN Pinle, ZENG Jianchao, CHAI Rui, WANG Lifang. Ultrasound thyroid segmentation network based on feature fusion and dynamic multi-scale dilated convolution [J]. Journal of Computer Applications, 2021, 41(3): 891-897.
[9]	YUAN Qianqian, DENG Hongmin, WANG Xiaohang. Citrus disease and insect pest area segmentation based on superpixel fast fuzzy C-means clustering and support vector machine [J]. Journal of Computer Applications, 2021, 41(2): 563-570.
[10]	YUAN Quan, WANG Yan, LI Yuxian. Fast algorithm for distance regularized level set evolution model [J]. Journal of Computer Applications, 2020, 40(9): 2743-2747.
[11]	GAO Haijun, ZENG Xiangyin, PAN Dazhi, ZHENG Bochuan. Rectal tumor segmentation method based on improved U-Net model [J]. Journal of Computer Applications, 2020, 40(8): 2392-2397.
[12]	WU Chongshu, LIN Lin, XUE Yunjing, SHI Peng. Hierarchical segmentation of pathological images based on self-supervised learning [J]. Journal of Computer Applications, 2020, 40(6): 1856-1862.
[13]	ZHANG Yifei, LI Xinfu, TIAN Xuedong. Stereo matching algorithm based on image segmentation [J]. Journal of Computer Applications, 2020, 40(5): 1415-1420.
[14]	ZENG Zhaoxin, LIU Jun. Microscopic image segmentation method of C.elegans based on deep learning [J]. Journal of Computer Applications, 2020, 40(5): 1453-1459.
[15]	WEI Xiaona, XING Jiaqi, WANG Zhenyu, WANG Yingshan, SHI Jie, ZHAO Di, WANG Hongzhi. Magnetic resonance image segmentation of articular synovium based on improved U-Net [J]. Journal of Computer Applications, 2020, 40(11): 3340-3345.