PENG Hanghang, XU Xuemei, LI Lixian, YIN linzi. Detection of Foreign Particles in Medical Solution Using Modified Affinity Propagation[J]. Chinese Journal of Electronics, 2018, 27(1): 140-149. doi: 10.1049/cje.2017.03.010
Citation: PENG Hanghang, XU Xuemei, LI Lixian, YIN linzi. Detection of Foreign Particles in Medical Solution Using Modified Affinity Propagation[J]. Chinese Journal of Electronics, 2018, 27(1): 140-149. doi: 10.1049/cje.2017.03.010

Detection of Foreign Particles in Medical Solution Using Modified Affinity Propagation

doi: 10.1049/cje.2017.03.010
Funds:  This work is supported by the National Natural Science Foundation of China (No.61502538) and the Graduate Scientific Research Foundation of Central South University (No.2016zzts226).
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  • Corresponding author: XU Xuemei (corresponding author) was born in Hunan Province, China, in 1971. She received both the B.S. degree and the M.S. degree from the University of Hunan Normal University, Changsha, China, and the Ph.D. degree of photoelectric engineering in the School of Physics and electronics from Hunan University, Changsha, China, from 1999 to 2005. (
  • Received Date: 2015-10-22
  • Rev Recd Date: 2016-06-14
  • Publish Date: 2018-01-10
  • A new detection method based on modified affinity propagation is proposed for detecting foreign particles in medical solution. The captured sequential images are used to generate moving trajectories of possible foreign particles. The external disturbances and background are almost removed by using a series of simple and effective image processing. The extracted potential targets, including foreign particles and possible residual noises, are partitioned into a number of valid clusters by modified affinity propagation clustering, in which the new similarity measure can better reflect the intrinsic link of linearstructured datasets and the temporal constraint enforces the temporal continuity of data points. The foreign particles can be easily recognized by analyzing the cluster structure according to the continuity and smoothness of moving targets trajectory. This algorithm combines modified affinity propagation method with the moving characteristics of different particles, and can achieve accurate partitions efficiently even though the clusters are badly adjacent. The experimental results indicate that the proposed algorithm can detect foreign particles effectively with high detection speed and accuracy.
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