Classification of human epithelial type 2 cell images using independent component analysis

Yang, Yan, Wiliem, Arnold, Alavi, Azadeh and Hobson, Peter (2013). Classification of human epithelial type 2 cell images using independent component analysis. In: 2013 IEEE International Conference on Image Processing ICIP 2013: Proceedings. 2013 IEEE International Conference on Image Processing (ICIP 2013), Melbourne, Australia, (733-737). 15-18 September 2013. doi:10.1109/ICIP.2013.6738151

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Author Yang, Yan
Wiliem, Arnold
Alavi, Azadeh
Hobson, Peter
Title of paper Classification of human epithelial type 2 cell images using independent component analysis
Conference name 2013 IEEE International Conference on Image Processing (ICIP 2013)
Conference location Melbourne, Australia
Conference dates 15-18 September 2013
Convener IEEE Signal Processing Society
Proceedings title 2013 IEEE International Conference on Image Processing ICIP 2013: Proceedings   Check publisher's open access policy
Journal name Proceedings of the International Conference on Image Processing   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2013
Sub-type Fully published paper
DOI 10.1109/ICIP.2013.6738151
Open Access Status
ISBN 9781479923410
ISSN 1522-4880
Start page 733
End page 737
Total pages 5
Collection year 2014
Language eng
Formatted Abstract/Summary
Identifying the presence of Anti-Nuclear Antibody in Human Epithelial type 2 (HEp-2) cells via Indirect Immunofluorescence (IIF) is commonly used to diagnose various connective tissue diseases in clinical pathology tests. This pathology test can be automated by computer vision algorithms. However, existing automated systems, namely Computer Aided Diagnostic (CAD) systems, are suffering numerous shortcomings such as using pre-selected features. To overcome such shortcomings, we propose a novel approach by learning filters from image statistics. Specifically, we train a filter bank from unlabelled cell images by using Independent Component Analysis (ICA). The filter bank is then applied to images to extract a set of filter responses. We extract regions from this set of responses and stack them into “cubic regions”. Average filter responses in 1 × 1, 2 × 2, 4 × 4 grids from the cubic-region are used as “ICA feature”. ICA features in multiple regions are stored in a feature collection matrix to represent each image. Finally, we use Support Vector Machine (SVM) in conjunction with histogram correlation kernel to classify cell images. We show that our approach outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.
Keyword HEp-2 cells classification
Independent component analysis
Indirect immunofluorescence
Anti-nuclear antibodies pathology test
Computer aided diagnostic
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Thu, 09 Jan 2014, 15:25:19 EST by Azadeh Alavi on behalf of School of Information Technol and Elec Engineering