Analysis of HEp-2 images using MD-LBP and MAD-bagging
Conference item
Authors | Schaefer, Gerald, Doshi, Niraj P., Zhu, Shao Ying and Hu, Qinghua |
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Abstract | Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using texture information in conjunction with a suitable classification system. In particular, we extract multidimensional local binary pattern (MD-LBP) texture features to characterise the cell area. These then form the input for a classification stage, for which we employ a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition. |
Keywords | Histograms; Cellular biophysics |
Year | 2014 |
Journal | Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Publisher | IEEE |
Digital Object Identifier (DOI) | https://doi.org/10.1109/EMBC.2014.6944562 |
Web address (URL) | http://hdl.handle.net/10545/620920 |
hdl:10545/620920 | |
ISBN | 9781424479290 |
File | File Access Level Open |
Publication dates | Aug 2014 |
Publication process dates | |
Deposited | 21 Nov 2016, 11:54 |
Contributors | Loughborough University and University of Derby |
https://repository.derby.ac.uk/item/92q5x/analysis-of-hep-2-images-using-md-lbp-and-mad-bagging
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