  Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence
for diagnosis of autoimmune diseases. Recently computer-aided diagnosis of
autoimmune diseases by IIF HEp-2 cell classification has attracted great
attention. However the HEp-2 cell classification task is quite challenging due
to large intra-class variation and small between-class variation. In this paper
we propose an effective and efficient approach for the automatic classification
of IIF HEp-2 cell image by fusing multi-resolution texture information and
richer shape information. To be specific, we propose to: a) capture the
multi-resolution texture information by a novel Pairwise Rotation Invariant
Co-occurrence of Local Gabor Binary Pattern (PRICoLGBP) descriptor, b) depict
the richer shape information by using an Improved Fisher Vector (IFV) model
with RootSIFT features which are sampled from large image patches in multiple
scales, and c) combine them properly. We evaluate systematically the proposed
approach on the IEEE International Conference on Pattern Recognition (ICPR)
2012, IEEE International Conference on Image Processing (ICIP) 2013 and ICPR
2014 contest data sets. The experimental results for the proposed methods
significantly outperform the winners of ICPR 2012 and ICIP 2013 contest, and
achieve comparable performance with the winner of the newly released ICPR 2014
contest.
