Benchmarking of image registration methods for differently stained histological slides
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327732" target="_blank" >RIV/68407700:21230/18:00327732 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8451040" target="_blank" >https://ieeexplore.ieee.org/document/8451040</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICIP.2018.8451040" target="_blank" >10.1109/ICIP.2018.8451040</a>
Alternative languages
Result language
angličtina
Original language name
Benchmarking of image registration methods for differently stained histological slides
Original language description
Image registration is a common task for many biomedical analysis applications. The present work focuses on the benchmarking of registration methods on differently stained histological slides. This is a challenging task due to the differences in the appearance model, the repetitive texture of the details and the large image size, between other issues. Our benchmarking data is composed of 616 image pairs at two different scales - average image diagonal 2.4k and 5k pixels. We compare eleven fully automatic registration methods covering the widely used similarity measures (and optimization strategies with both linear and elastic transformation). For each method, the best parameter configuration is found and subsequently applied to all the image pairs. The performance of the algorithms is evaluated from several perspectives - the registrations (in)accuracy on manually annotated landmarks, the method robustness and its processing computation time.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-15361S" target="_blank" >GA17-15361S: Learning local concepts from global training data for biomedical image segmentation and classification</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Image Processing
ISBN
978-1-4799-7061-2
ISSN
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e-ISSN
2381-8549
Number of pages
5
Pages from-to
3368-3372
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
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Event location
Atény
Event date
Oct 7, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000455181503097