Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching
Data(s) |
01/07/2014
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Resumo |
This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset. |
Formato |
application/pdf |
Identificador | |
Publicador |
Elsevier |
Relação |
http://eprints.qut.edu.au/71632/1/wiliem_hep2_cell_classification_pr_2014.pdf DOI:10.1016/j.patcog.2013.10.014 Wiliem, Arnold, Sanderson, Conrad, Wong, Yongkang, Hobson, Peter, Minchin, Rodney F., & Lovell, Brian C. (2014) Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching. Pattern Recognition, 47(7), pp. 2315-2324. |
Direitos |
Copyright 2013 Elsevier Ltd. NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, [47, 7, (July 2014)] http://dx.doi.org/10.1016/j.patcog.2013.10.014 |
Fonte |
Science & Engineering Faculty |
Palavras-Chave | #010200 APPLIED MATHEMATICS #060100 BIOCHEMISTRY AND CELL BIOLOGY #060102 Bioinformatics #080104 Computer Vision #080106 Image Processing #080109 Pattern Recognition and Data Mining #090609 Signal Processing #111601 Cell Physiology #Indirect Immunofluorescence tests #Bag of visual words #HEp-2 cell classification |
Tipo |
Journal Article |