An optimization approach to scanning skin direct immunofluorescence specimens

Samak, Asser, Wiliem, Arnold, Hobson, Peter, Walsh, Michael, Ditchmen, Ted, Troskie, Arne, Barksdale, Sarah, Edwards, Rhonda, Jennings, Anthony and Lovell, Brian C. (2015). An optimization approach to scanning skin direct immunofluorescence specimens. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). International Conference on Digital Image Computing: Techniques and Applications, DICTA, Adelaide, SA, Australia, (130-137). 23-25 November 2015. doi:10.1109/DICTA.2015.7371230

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Author Samak, Asser
Wiliem, Arnold
Hobson, Peter
Walsh, Michael
Ditchmen, Ted
Troskie, Arne
Barksdale, Sarah
Edwards, Rhonda
Jennings, Anthony
Lovell, Brian C.
Title of paper An optimization approach to scanning skin direct immunofluorescence specimens
Conference name International Conference on Digital Image Computing: Techniques and Applications, DICTA
Conference location Adelaide, SA, Australia
Conference dates 23-25 November 2015
Convener Jamie Sherrah
Proceedings title 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Journal name 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/DICTA.2015.7371230
Open Access Status Not Open Access
ISBN 9781467367950
Start page 130
End page 137
Total pages 8
Collection year 2016
Language eng
Abstract/Summary We propose an optimization framework for developing a fully automated scanning system. The framework allows us to have effective design choices in developing the system as every choice should be based on optimizing the objective function. We apply this framework in developing a fully automated scanning system for skin Direct Immunofluorescence (DIF) test. To that end, we introduce both non-algorithmic and algorithmic methods to optimize the objective function. Whilst the non-algorithmic methods comprise various design choices that could indirectly optimize the framework, the algorithmic methods primarily aim to optimize the objective by computing an optimal scan plan. In this work, we explore two algorithmic methods: (1) a heuristic sliding region approach and (2) a quad-tree approach. To our knowledge, this is one of the first works to describe a fully automated scanning system for skin DIF tests. As such, we propose a novel dataset that is hoped to stimulate the research interest in developing digitizing systems for skin DIF tests. All the described methods were evaluated on this novel dataset. Our scanning system is now part of a digital pathology system which has been fully deployed and routinely used within a pathology laboratory.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Fri, 11 Dec 2015, 12:50:54 EST by Arnold Wiliem on behalf of School of Information Technol and Elec Engineering