A multiple covariance approach for cell detection of Gram-stained smears images

Crossman, Matthew, Wiliem, Arnold, Finucane, Paul, Jennings, Anthony and Lovell, Brian C. (2015). A multiple covariance approach for cell detection of Gram-stained smears images. In: 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings. International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Conference, Brisbane, QLD, Australia, (932-936). 19-24 April 2015. doi:10.1109/ICASSP.2015.7178106


Author Crossman, Matthew
Wiliem, Arnold
Finucane, Paul
Jennings, Anthony
Lovell, Brian C.
Title of paper A multiple covariance approach for cell detection of Gram-stained smears images
Conference name International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Conference
Conference location Brisbane, QLD, Australia
Conference dates 19-24 April 2015
Convener IEEE
Proceedings title 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings   Check publisher's open access policy
Journal name ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE (Institute for Electrical and Electronic Engineers)
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/ICASSP.2015.7178106
Open Access Status Not yet assessed
ISBN 9781467369978
ISSN 1520-6149
Volume 2015-August
Start page 932
End page 936
Total pages 5
Collection year 2016
Language eng
Formatted Abstract/Summary
Microscope examination of Gram stained clinical specimens is used for aiding the diagnosis of patients with infectious diseases. In high volume pathology laboratories, this manual microscopy examination is considered time consuming and labour intensive. Unfortunately, despite the great benefits offered from the application of Computer Aided Diagnosis (CAD) systems, to our knowledge, the highest automation stage for Gram stained slide analysis is only at the pre-analytical process. This paper takes the first steps towards the application of computer vision to direct smear, Gram stained images. To that end, we present a novel Gram stain image dataset. In addition, we also propose a multiple covariance approach for leukocyte and epithelial cell detection in Gram stain images. Each covariance matrix represents a particular image region characterising the cell's deformed structure. As covariance matrices form points on an Symmetric Positive Definite (SPD) manifold, the traditional Euclidean-based analysis cannot be used. As such, we first map the manifold points into the Reproducing Kernel Hilbert Space (RKHS). The analysis is done via a novel kernel similarity function that allows comparison between sets of covariance matrices. The proposed approach is contrasted, in the proposed dataset, with two recent state of the art methods in pedestrian detection: Histogram Of Gradient (HOG) and the traditional single covariance matrix approach. We found that the proposed approach outperformed both of these methods.
Keyword Gram stain analysis
Direct smears
Cell detection
Riemannian manifolds
Symmetric Positive Definite Matrix group
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Tue, 25 Aug 2015, 11:43:04 EST by Anthony Yeates on behalf of School of Information Technol and Elec Engineering