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author:

Zeng, N. (Zeng, N..) [1] | Wang, Z. (Wang, Z..) [2] | Zhang, H. (Zhang, H..) [3] | Kim, K.-E. (Kim, K.-E..) [4] | Li, Y. (Li, Y..) [5] | Liu, X. (Liu, X..) [6]

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Scopus

Abstract:

In this paper, a novel statistical pattern recognition method is proposed for accurately segmenting test and control lines from the gold immunochromatographic strip (GICS) images for the benefits of quantitative analysis. A new dynamic state-space model is established, based on which the segmentation task of test and control lines is transformed into a state estimation problem. Especially, the transition equation is utilized to describe the relationship between contour points on the upper and the lower boundaries of test and control lines, and a new observation equation is developed by combining the contrast of between-class variance and the uniformity measure. Then, an innovative particle filter (PF) with a hybrid proposal distribution, namely, deep-belief-network-based particle filter (DBN-PF) is put forward, where the deep belief network (DBN) provides an initial recognition result in the hybrid proposal distribution, and the particle swarm optimization algorithm moves particles to regions of high likelihood. The performance of proposed DBN-PF method is comprehensively evaluated on not only an artificial dataset but also the GICS images in terms of several indices as compared to the PF and DBN methods. It is demonstrated via experiment results that the proposed approach is effective in quantitative analysis of GICS. © 2019 IEEE.

Keyword:

deep belief network; dynamical model; Gold immunochromatographic strip; image segmentation; Monte Carlo; particle filter; particle swarm optimization algorithm; proposal distribution

Community:

  • [ 1 ] [Zeng, N.]Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, China
  • [ 2 ] [Wang, Z.]Department of Computer Science, Brunel University London, Uxbridge, United Kingdom
  • [ 3 ] [Zhang, H.]Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, China
  • [ 4 ] [Kim, K.-E.]Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
  • [ 5 ] [Li, Y.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China
  • [ 6 ] [Liu, X.]Department of Computer Science, Brunel University London, Uxbridge, United Kingdom

Reprint 's Address:

  • [Wang, Z.]Department of Computer Science, Brunel University LondonUnited Kingdom

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Source :

IEEE Transactions on Nanotechnology

ISSN: 1536-125X

Year: 2019

Volume: 18

Page: 819-829

2 . 1 9 6

JCR@2019

2 . 1 0 0

JCR@2023

ESI HC Threshold:150

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 150

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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