Benchmarking of image registration methods for differently stained histological slides

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Benchmarking of image registration methods for differently stained histological slides

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Benchmarking of image registration methods for differently stained histological slides

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-15361S" target="_blank" >GA17-15361S: Učení lokálních konceptů z globálních trénovacích dat pro klasifikaci a segmentaci biomedicínských obrazů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Conference on Image Processing

ISBN

978-1-4799-7061-2

ISSN

—

e-ISSN

2381-8549

Počet stran výsledku

5

Strana od-do

3368-3372

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

—

Místo konání akce

Atény

Datum konání akce

7. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000455181503097