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Title: Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach
Authors: Zeng, N
Wang, Z
Zineddin, B
Li, Y
Du, M
Xiao, L
Liu, X
Young, T
Keywords: Cellular neural networks;Gold immuno chromatographic strip;Image segmentation;Mathematical morphology;Switching particle swarm optimization
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: IEEE Transactions on Medical Imaging, 33 (5): 1129 - 1136, (May 2014)
Abstract: Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. 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, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. 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, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.
Description: "(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
ISSN: 0278-0062
Appears in Collections:Dept of Computer Science Research Papers

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