Segmentation and Recognition of DNA Robots in AFM Images

doi:10.3778/j.issn.1002-8331.1803-0268

Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (11): 192-198.DOI: 10.3778/j.issn.1002-8331.1803-0268

Previous Articles Next Articles

Segmentation and Recognition of DNA Robots in AFM Images

TONG Lin1，2, HAN Yuexing1，3, Akihiko Konagaya3，4

1.School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2.Shanghai Advanced Research Institute, University of Chinese Academy of Sciences, Shanghai 200000, China
3.Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Tokyo 226-8502, Japan
4.National Institute of Informatics, Tokyo 101-8430, Japan

Online:2019-06-01 Published:2019-05-30

DNA机器人在AFM图像中的分割和识别

童麟1，2，韩越兴1，3，小长谷明彦3，4

1.上海大学计算机工程与科学学院，上海 200444
2.中国科学院大学上海高等研究院，上海 200000
3.东京工业大学综合理工研究科，东京 226-8502
4.国立情报学研究所，东京 101-8430

Abstract

Abstract: DNA robot is a type of nano robots made from DNA macromolecules with the origami technology. DNA robots can be used for the diagnosis and treatment of the cancer. To research the DNA robots, Atomic Force Microscope（AFM） is used to take the photographs of the DNA robots. Since there are many noises in the AFM images and overlaps among the DNA robots, removing noises, segmentation, and recognition are processed. The watershed based on multi-scale technique is improved for the segmentation of DNA robots. DNA robots are classified and recognized with the Fourier descriptors, Curvature Scale Space（CSS） method and Hu moment.

Key words: Atomic Force Microscope（AFM） image, watershed algorithm, DNA robots, object recognition, noise elimination

摘要： DNA机器人是一种由DNA大分子通过折纸技术制作的纳米级别的机器人，可以用于癌症的诊断和治疗。为了对DNA机器人形态进行研究，研究人员利用原子力显微镜（Atomic Force Microscope，AFM）拍摄出机器人的AFM图像。针对AFM图像多噪声及被观察的DNA机器人大规模重叠的特点，从除噪、分割、识别三个过程展开，提出了一种基于多尺度改进的分水岭AFM图像切割算法，以傅里叶描述子、曲率尺度空间描述符和Hu矩作为特征，实现DNA纳米机器人的分类和识别。

关键词: 原子力显微镜（AFM）图像, 分水岭算法, DNA机器人, 物体识别, 噪声消除

TONG Lin1，2, HAN Yuexing1，3, Akihiko Konagaya3，4. Segmentation and Recognition of DNA Robots in AFM Images[J]. Computer Engineering and Applications, 2019, 55(11): 192-198.

童麟1，2，韩越兴1，3，小长谷明彦3，4. DNA机器人在AFM图像中的分割和识别[J]. 计算机工程与应用, 2019, 55(11): 192-198.

[1]	YU Tingsong, WEN Yuanmei, LING Yongquan. Object Recognition Based on Tensor Decomposition Fusing RGB-D Image [J]. Computer Engineering and Applications, 2019, 55(2): 174-178.
[2]	ZHU Jiong, YAN Lijun, MA Yan. Development and Application of Improved SSIM Image Evaluation System [J]. Computer Engineering and Applications, 2019, 55(12): 215-219.
[3]	YAN Hongwen, LU Geyu. Application research on complete ensemble empirical mode decomposition, wavelet transform and convolutional neural networks in short-term wind speed forecasting [J]. Computer Engineering and Applications, 2018, 54(9): 224-230.
[4]	LIU Xiangnan, WANG Yutan, ZHAO Chen, ZHU Chaowei, LI Lekai. Research on image segmentation method of Lingwu Long Jujubes based on watershed [J]. Computer Engineering and Applications, 2018, 54(15): 169-175.
[5]	FEI Yunqiao1, LIU Wenping1, LUO Youqing2, LU Pengfei2. Comparison of algorithms for unmanned aerial vehicle image segmentation in monitoring forest diseases and insect pests [J]. Computer Engineering and Applications, 2017, 53(8): 216-223.
[6]	QI Mei1, HU Min2. Object recognition based on multi-direction spatial Bags of Words model [J]. Computer Engineering and Applications, 2017, 53(7): 197-201.
[7]	DUAN Qi1, ZHANG Dongbo1,2, HU Yang1. Feeding system for lacquer?immersing drying?furnace with guide of machine vision [J]. Computer Engineering and Applications, 2017, 53(7): 225-230.
[8]	WU Fan, MIN Huasong. Real-time 3D semantic map generation method [J]. Computer Engineering and Applications, 2017, 53(6): 67-72.
[9]	ZHANG Feng1, GUO Rui1, CHENG Zhiyong2, YONG Jun1, FU Siyao3, HAN Liwei3, YANG Jun3, JIA Legang3. Detection for transmission line obstacles based on principal component gradient histogram [J]. Computer Engineering and Applications, 2016, 52(15): 254-259.
[10]	XU Tianzhi, ZHANG Guicang, JIA Yuan. Color image segmentation based on morphology gradients and watershed algorithm [J]. Computer Engineering and Applications, 2016, 52(11): 200-203.
[11]	ZHU Mingwu, HAN Jun, LU Dongming, BAN Xiaokun. Object recognition method based on local contour feature in natural scenario [J]. Computer Engineering and Applications, 2016, 52(1): 162-167.
[12]	SUN Jinbo, YU Suihuai, CHEN Dengkai. Object recognition method based on multi-feature fusion of D-S evidence theory [J]. Computer Engineering and Applications, 2015, 51(9): 147-151.
[13]	ZHU Ping1, MEI Jie2, ZHU Xiaobo3, HUANG Yonggang3. Segmentation method of CT cardiac image based on bilateral filtering and watershed algorithm [J]. Computer Engineering and Applications, 2015, 51(8): 170-173.
[14]	SUN Bin, ZHANG Changsheng, LIU Ziyu, QIAN Bin, ZHANG Hanping. Study of HVPP algorithm for pointer dial visual detection [J]. Computer Engineering and Applications, 2015, 51(23): 189-194.
[15]	GUO Wei, LI Xijun, WEN Tianyi. Watershed algorithm combining multi-structure filter and multi-scale reconstruction [J]. Computer Engineering and Applications, 2015, 51(14): 151-157.

Segmentation and Recognition of DNA Robots in AFM Images

DNA机器人在AFM图像中的分割和识别

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics