Detection of Foreign Matter in Transfusion Solution Based on Gaussian Background Modeling and an Optimized BP Neural Network

Sensors

Fuqiang Zhou¹  Zhen Su²  Xinghua Chai² 

[1] School of Instrumentation Science and Optoelectronics Engineering, Beihang University, XueYuan Road No. 37, Haidian District, Beijing 100191, China;

关键词: detection of foreign matter;    Gaussian background model;    mind evolutionary algorithm;    BP neural network;    ReliefF feature selection;   

DOI  :  10.3390/s141119945

来源: mdpi

【 摘 要 】

This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.