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Verfasst von:Rojas-Moraleda, Rodrigo [VerfasserIn]   i
 Xiong, Wei [VerfasserIn]   i
 Valous, Nektarios A. [VerfasserIn]   i
 Halama, Niels [VerfasserIn]   i
Titel:Segmentation of biomedical images based on a computational topology framework
Verf.angabe:Rodrigo Rojas Moraleda, Wei Xiong, Nektarios A. Valous, Niels Halama
E-Jahr:2020
Jahr:2 December 2020
Umfang:7 S.
Fussnoten:Gesehen am 02.02.2021
Titel Quelle:Enthalten in: Seminars in immunology
Ort Quelle:London : Academic Press, 1993
Jahr Quelle:2020
Band/Heft Quelle:48(2020,1) Artikel-Nummer 101432, 7 Seiten
ISSN Quelle:1096-3618
Abstract:The homology groups of a topological space provide us with information about its connectivity and the number and type of holes in it. This type of information can find practical applications in describing the intrinsic structure of an image, as well as in identifying equivalence classes in collections of images. When computing homological characteristics, the existence and strength of the relationships between each pair of points in the topological space are studied. The practical use of this approach begins by building a topological space from the image, in which the computation of the homology groups can be carried out in a feasible time. Once the homological properties are obtained, what follows is the task of translating such information into operations such as image segmentation. This work presents a technique for denoising persistent diagrams and reconstructing the shape of segmented objects using the remaining classes on the diagram. A case study for the segmentation of cell nuclei in histological images is used for demonstration purposes. With this approach: a) topological denoising is achieved by aggregating trivial classes on the persistence diagram, and b) a growing seed algorithm uses the information obtained during the construction of the persistence diagram for the reconstruction of the segmented cell structures.
DOI:doi:10.1016/j.smim.2020.101432
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1016/j.smim.2020.101432
 Volltext: http://www.sciencedirect.com/science/article/pii/S1044532320300488
 DOI: https://doi.org/10.1016/j.smim.2020.101432
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Computational topology
 Image analysis
 Natural patterns in data
 Topological data analysis
K10plus-PPN:1746732369
Verknüpfungen:→ Zeitschrift

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