Paper Info
Title
HEp-2 Cell Classification via Fusing Texture and Shape Information.
Abstract
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.
Year
Venue
Field
2015
CoRR
Computer vision,Pairwise comparison,Data set,Binary pattern,Pattern recognition,Fisher vector,Computer science,Image processing,Artificial intelligence,Invariant (mathematics),Indirect immunofluorescence
DocType
Volume
Citations 
Journal
abs/1502.04658
1
PageRank 
References 
Authors
0.37
24
5
Name
Order
Citations
PageRank
Xianbiao Qi11038.25
Guoying Zhao23767166.92
Chun-Guang Li331017.35
Jun Guo48110.54
Matti Pietikäinen514779739.80