X. Qi, G. Zhao, C. Li, J. Guo and M. Pietikäinen, "HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 2, pp. 429-440, March 2017. doi: 10.1109/JBHI.2015.2508938
HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information
|Author:||Qi, Xianbiao1,2; Zhao, Guoying3; Li, Chun-Guang1;|
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2University of Oulu, FIN-90014, Oulu, Finland
3Center for Machine Vision and Signal Analysis, University of Oulu, FIN-90014, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019040811355
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-04-08
Indirect immunofluorescence imaging of human epithelial type 2 (HEp-2) cell image is an effective evidence to diagnose autoimmune diseases. Recently, computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has attracted great attention. However, the HEp-2 cell classification task is quite challenging due to large intraclass and small interclass variations. In this paper, we propose an effective approach for the automatic HEp-2 cell classification by combining multiresolution co-occurrence texture and large regional shape information. To be more specific, we propose to: 1) capture multiresolution co-occurrence texture information by a novel pairwise rotation-invariant co-occurrence of local Gabor binary pattern descriptor; 2) depict large regional shape information by using an improved Fisher vector model with RootSIFT features, which are sampled from large image patches in multiple scales; and 3) combine both features. We evaluate systematically the proposed approach on the IEEE International Conference on Pattern Recognition (ICPR) 2012, the IEEE International Conference on Image Processing (ICIP) 2013, and the ICPR 2014 contest datasets. The proposed method based on the combination of the introduced two features outperforms the winners of the ICPR 2012 contest using the same experimental protocol. Our method also greatly improves the winner of the ICIP 2013 contest under four different experimental setups. Using the leave-one-specimen-out evaluation strategy, our method achieves comparable performance with the winner of the ICPR 2014 contest that combined four features.
IEEE journal of biomedical and health informatics
|Pages:||429 - 440|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
The work of X. Qi, G. Zhao, and M. Pietikäinen was supported by the Academy of Finland and Infotech Oulu. The work of C.-G. Li and J. Guo was supported by the National Natural Science Foundation of China under Grants 61273217 and 61175011, and the 111 project under Grant B08004 from the Ministry of Education of China.
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