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Paper ThCT2.2

Snell, Violet (University of Surrey), Christmas, William (University of Surrey), Kittler, Josef (University of Surrey)

Texture and Shape in Fluorescence Pattern Identification for Auto-Immune Disease Diagnosis

Scheduled for presentation during the Regular Session "Imaging and Segmentation" (ThCT2), Thursday, November 15, 2012, 14:50−15:10, Multi-Purpose Hall

21st International Conference on Pattern Recognition, November 11-15, 2012, Tsukuba International Congress Center, Tsukuba, Japan

This information is tentative and subject to change. Compiled on July 20, 2018

Keywords Medical Image Analysis and Registration

Abstract

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 so far has eluded reliable replication through automated measurements. We introduce a combination of spectral analysis and multi-scale digital filtering to extract the most discriminative variables from the cell images. We also apply multi-stage classification techniques to make optimal use of the limited labelled data set. Overall error rate of 1.6% is achieved in recognition of 6 different cell patterns, which drops to 0.5% if only positive samples are considered.

 

 
  
 

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