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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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

  • Event location

    Atény

  • Event date

    Oct 7, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000455181503097