Abstract
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.
Highlights
Medical transfusion is one of the important preparations of the pharmaceutical industry in China
When foreign matter appears in the reflective regions in the bottom of the bottle, the visible foreign matter is difficult to detect after the inter-frame difference method, as shown in Figure 6(d1–d4), because its energy is weakened deeply and it is drowned out by the complex background
This paper mainly describes a new method for the detection and identification of foreign matter in transfusion solution
Summary
Medical transfusion is one of the important preparations of the pharmaceutical industry in China. Moghadas et al [8] proposed a non-rotating method, which was based on stereo vision, to acquire moving objects and used a Multi-layer Perceptron (MLP) neural network to distinguish foreign matter and bubbles in a vial. This method may be confused by spurious light and the instability of the light source. The Gaussian background modeling method is applied to detect foreign matter firstly, and it can accurately extract moving objects while suppressing background noise.
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