UNet model in image reconstruction for electrical impedance tomography
Brak miniatury
Data
2022
Inny tytuł
Typ
Artykuł recenzyjny (pokonferencyjny)
Redaktor
dc.contributor.advisor
Dyscyplina PBN
Informatyka techniczna i telekomunikacja
Czasopismo lub seria
PRZEGLĄD ELEKTROTECHNICZNY
ISSN
0033-2097
ISBN
DOI
10.15199/48.2022.04.26
Strona internetowa
Wydawca
Wydawca
Wydanie
Numer
Strony od-do
Tytuł monografii
item.page.defence
Tytuł tomu
Opis
Rodzaj licencji
Abstrakt (en)
This paper presents a new algorithm where the UNet convolutional neural network was used to correct deterministic algorithm results, as was is another similar solution using the DBar deterministic algorithm. Instead of the DBar algorithm, another EIT reconstruction algorithm was used in the context cooperation with impedance tomography to extract details in EIT reconstruction. The algorithm uses machine learning to improve the tomographic images obtained with the deterministic algorithm. The final result contains much less noise, and the position of the objects is much better defined, unlike in the deterministic approach. Furthermore, the paper shows how the reconstruction obtained with the hybrid tomograph can be improved to show more details. This paper aims to present a solution that will be used in the context of medical tomography, where the EIT system and the developed algorithm will be used to obtain high-resolution tomography images of the bladder.