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HEp-2 fluorescence pattern classification

Snell, V, Christmas, W and Kittler, J (2014) HEp-2 fluorescence pattern classification PATTERN RECOGNITION, 47 (7). pp. 2338-2347.

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Automation of HEp-2 cell pattern classification would drastically improve the accuracy and throughput of diagnostic services for many auto-immune diseases, but it has proven difficult to reach a sufficient level of precision. Correct diagnosis relies on a subtle assessment of texture type in microscopic images of indirect immunofluorescence (IIF), which has, so far, eluded reliable replication through automated measurements. Following the recent HEp-2 Cells Classification contest held at ICPR 2012, we extend the scope of research in this field to develop a method of feature comparison that goes beyond the analysis of individual cells and majority-vote decisions to consider the full distribution of cell parameters within a patient sample. We demonstrate that this richer analysis is better able to predict the results of majority vote decisions than the cell-level performance analysed in all previous works. © 2013.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Snell, V
Christmas, W
Kittler, J
Date : 1 July 2014
DOI : 10.1016/j.patcog.2013.10.012
Copyright Disclaimer : © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Uncontrolled Keywords : Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, ENGINEERING, ELECTRICAL & ELECTRONIC, IIF image, HEp-2 pattern, Texture
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 04 Nov 2014 12:59
Last Modified : 31 Oct 2017 17:02

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