Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology

Fuzhou, China

Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology

Fuzhou, China
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Zeng N.,Fuzhou University | Zeng N.,University of Hong Kong | Hung Y.S.,University of Hong Kong | Li Y.,Fuzhou University | And 3 more authors.
Expert Systems with Applications | Year: 2014

This paper presents a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for quantification analysis of the lateral flow immunoassay (LFIA), which represents the first attempt to estimate the concentration of target analyte based on the well-established state-space model. A new switching local evolutionary PSO (SLEPSO) is developed and analyzed. The velocity updating equation jumps from one mode to another based on the non-homogeneous Markov chain, where the probability transition matrix is updated by calculating the diversity and current optimal solution. Furthermore, DE mutation and crossover operations are implemented to improve local best particles searching in PSO. Compared with some well-known PSO algorithms, the experiments results show the superiority of proposed SLEPSO. Finally, the new SLEPSO is successfully exploited to quantification analysis of the LFIA system, which is essentially nonlinear and dynamic. Therefore, this can provide a new method for the area of quantitative interpretation of LFIA system. © 2013 Elsevier Ltd. All rights reserved.


Zhang J.,Fuzhou University | Zhang J.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology | Du M.,Fuzhou University | Du M.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology
2012 5th International Congress on Image and Signal Processing, CISP 2012 | Year: 2012

Gold immunochromatographic strip (GICS) quantitative detective can provide more information than the qualitative or semiquantitative testing. In this paper, a fast color image segmentation method is presented to develop the quantitative detective of GICS. The image of GICS was acquired by Charge-coupled Device (CCD) image sensor, and segmented by fuzzy c-means (FCM) clustering algorithm based on color histogram in HSV color space. Maximin-distance algorithm was adopted to get the initial positions of centroids and cluster number to overcome the shortcoming that the FCM algorithm may produce local optimal results. For the segmented target image, a special characteristic parameter was constructed and calculated in HSV color space to achieve the quantitative interpretation of the GICS. © 2012 IEEE.


Li Y.,Fuzhou University | Zeng N.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology | Du M.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology
Journal of Computers | Year: 2011

Gold immunochromatographic strip assay is a rapid, simple, single-copy and on-site method. Quantitative Interpretation of the strip can provide more information than the traditional qualitative or semiquantitative strip assay. The paper aims to develop an image based assay method for quantitative determination of trace concentrations by gold immunochromatographic strip. The image of gold immunochromatographic strip is taken by CCD, and, after the proper filter and window cutting, the test line and control line is segmented by the genetic fast fuzzy c-means(FCM) clustering algorithm. In order to improve the measure property, based on Lambert-beer law, the relative reflective integral optical density(RIOD) is selected as the feature by which the interference in the test and control lines can be canceled out each other. The proposed method is applied to the quantitative detection of human chorionic gonadotropin (hCG) as a model. Firstly, the segmentation performance of the genetic fast FCM clustering algorithm is compared with threshold method and FCM clustering algorithm in terms of the peak signalto- noise ratio (PSNR). Furthermore, the comparison of the blind experiment between the proposed method and commercial quantitative instrument swp-sc1 is carried out. This method is shown to deliver a result comparable and even superior to existing techniques. © 2011 ACADEMY PUBLISHER.


Chen X.,Fuzhou University | Chen X.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology | Du M.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | Year: 2012

Traditional PCR instrument detects the temperature of reagent with contact measurement, which has several limitations such as thermal hysteresis, reagent contamination and point-measurement. Thus an infra red thermometer is applied to contactlessly measure the temperature of reagent. But infra red thermometer has the defect of relatively high measurement noise. Therefore, we propose a sequential dual Kalman filter (Seq-DKF) algorithm that uses an iterated extended Kalman filter (IEKF) and a linear Kalman filter (KF) to remove the measurement noise from infra red thermometer signals in real-time. In this algorithm, IEKF and KF are executed sequentially. The former filter rapidly identifies the parameter of heat exchange model with a small number of observations. The latter one filters the temperature signal based on the identified model. After specifying the heat exchange model and the Seq-DKF, we apply the algorithm to the PCR instrument for testing. Finally, the other three filters are employed for comparison to demonstrate the effectiveness of the Seq-DKF algorithm.


Zeng N.,Fuzhou University | Wang Z.,Brunel University | Li Y.,Fuzhou University | Li Y.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology | And 3 more authors.
IEEE Transactions on Biomedical Engineering | Year: 2011

In this paper, a mathematical model for sandwich-type lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extended Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also for inspecting the effects from various design parameters in both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes in the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize, and design the properties of lateral flow immunoassay devices. © 2006 IEEE.


Zeng N.,Fuzhou University | Li Y.,Fuzhou University | Du M.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology
Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010 | Year: 2010

In this paper, a CCD-based imaging gold immunochromatographic assay system combining genetic fast FCM algorithm is developed for rapid quantitative detection of human chorionic gonadotropin(hCG). The image of gold immunochromatographic strip is taken by CCD sensor, and after using the genetic fast FCM algorithm to precisely extract the test and control lines of strips, the reflective integral optical density(IOD) is selected as the feature based on Lambert-beer law. The ratio (IODt/IODc) is directly proportional to the concentration of hCG in a sample by which the interference in the test and control lines can be cancelled out each other. We observe a good linearity(correlation of coefficient r = 0.98995) from the construction of standard curve throughout the entire measuring range 0-500mIU/ml, and the detection limit of the proposed method is enough sensitive to detect the hCG in the blood or urine of the pregnancy woman. The precision of the intra-assay expressed as coefficient of variation(CV) is below 12%. The system presented here takes less than 10min to perform from the sample treatment to the data analysis. Relative to scanning reflective optical system, our results suggest that a simple CCD-based imaging system can speed assay development, reduce errors, and improve accuracy. ©2010 IEEE.


Zeng N.,Fuzhou University | Zeng N.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology | Wang Z.,Brunel University | Li Y.,Fuzhou University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. Comparing to the traditional qualitative or semi-quantitative method, a completely quantitative interpretation of the strips can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm. It is shown that the CNN offers a robust method for accurately segmenting the test and control lines via adaptively setting the threshold value, and therefore serves as a novel image methodology for the interpretation of GICS. © 2012 Springer-Verlag.


Jiang H.,Fuzhou University | Du M.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 | Year: 2011

The gold immunochromatographic assay has the advantages of simple operation, low costs and rapid operation time. But the traditional immunochromatographic strip can only get qualitative or semi-quantitative results observed directly with the naked eyes, the disadvantages are low measurement accuracy, and it is difficult to achieve quantitative measurement. This paper presents a new method to perform the unsupervised classification for the gold immunochromatographic strip by combining the histogram features vectors and the fuzzy C-means algorithm based on computer image analysis system. Then provide procedures of extracting the histogram features as input vectors for fuzzy C-means algorithm and example of the fuzzy C-means clustering analysis methods of the gold immunochromatographic strip. In the experiment, the discrimination coefficient Classification coefficient F is 0.8953 and the Average fuzzy entropy H=0.085, the gold immunochromatographic strips with various human chorionic gonadotropin (hCG) concentrations were accurate and unsupervised classified to three clustering. The result proves that the classification of the gold immunochromatographic strips by the histogram features vectors and the fuzzy C-means algorithm is reasonable and validated, it offers a good semiquantitative and quantitative test method to the immunochromatographic strip for clinical diagnosis. The research can not only enhance the detection sensitivity and the objectivity of test result, but also have a sound application value. © 2011 IEEE.


Chen X.,Fujian Electric Power Survey and Design Institute | Du M.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology
2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012 | Year: 2012

In this work we describe the design and engineering of a multi-channel adaptive electrochemical (EC) signal acquisition system both using Field Programmable Gate Array (FPGA) and Field Programmable Analog Array (FPAA). With inheriting the powerful properties from the above devices, it has both automatic gain and reconfigurable feature. The 16 uniform automatic gain control (AGC) modules in this system operate in parallel and set the gain automatically dependent on the input amplitude. Compared with most of the other AGC systems, the combined FPGA/FPAA implementation presented here operates without ADC sampling and software processing, so that the gain-setting time is minimized. Besides, the FPAA-based AGC system can be dynamically reconfigured for polymerase chain reaction (PCR) in future design without changing any hardware. For the simple but efficient control mechanism, most circuits of the system can be integrated into a small-scale FPGA chip and several FPAA chips. These characters make this detection system ideal for in-situ or field multi-channel EC-PCR measurements. © 2012 IEEE.


Zeng N.,Fuzhou University | Zeng N.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology | Wang Z.,Brunel University | Li Y.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology | And 2 more authors.
IEEE Transactions on Nanotechnology | Year: 2012

In this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state-space model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay. © 2011 IEEE.

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