Paper Info
Title
Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach.
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.
Year
DOI
Venue
2014
10.1109/TMI.2014.2305394
IEEE Trans. Med. Imaging
Keywords
Field
DocType
spso-based cnn,semiquantitative strip assay,gold,single-copy target analyte detection,canny operator,control line segmentation,mathematical morphology method,gics reading-window extraction,gold immuno chromatographic strip (gics),test line segmentation,particle swarm optimisation,image segmentation,cnn performance,gics image,rapid target analyte detection,gold immunochromatographic strip assay,mathematical morphology,spso algorithm,feature extraction,gics reading-window detection,edge detection,cellular neural nets,cnn template parameter,cnn algorithm,peak signal-to-noise ratio,au,on-site target analyte detection,switching particle swarm optimization,gics interpretation,qualitative strip assay,strips,image-based quantitative analysis,robust method,chromatography,medical image processing,quantitatively trace concentration determination,cellular neural network approach,cellular neural networks (cnns),cellular neural networks,peak signal to noise ratio,algorithm design and analysis,immune system,neural networks
Particle swarm optimization,Computer vision,Mathematical morphology,Edge detection,Computer science,Feature extraction,Image segmentation,STRIPS,Artificial intelligence,Analyte,Cellular neural network
Journal
Volume
Issue
ISSN
33
5
1558-254X
Citations 
PageRank 
References 
22
0.86
13
Authors
8
Name
Order
Citations
PageRank
Nianyin Zeng127821.91
Zidong Wang211003578.11
Bachar Zineddin3462.91
Yurong Li423416.14
Min Du514314.67
Liang Xiao643165.25
Xiaohui Liu75042269.99
Terry Young832323.44