Pregled bibliografske jedinice broj: 1257032
Automating Regularization Parameter Selection of the Inverse Problem in Ultrasound Tomography
Automating Regularization Parameter Selection of the Inverse Problem in Ultrasound Tomography // 2022 IEEE International Ultrasonics Symposium (IUS)
Venecija, Italija: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 1-4 doi:10.1109/IUS54386.2022.9957277 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1257032 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Automating Regularization Parameter Selection of the
Inverse Problem in Ultrasound Tomography
Autori
Carević, Anita ; Slapničar, Ivan ; Almekkawy, Mohamed
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2022 IEEE International Ultrasonics Symposium (IUS)
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2022, 1-4
ISBN
978-1-6654-7813-7
Skup
2022 IEEE International Ultrasonics Symposium (IUS)
Mjesto i datum
Venecija, Italija, 10.10.2022. - 13.10.2022
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Automating Regularization Parameter Selection of the Inverse Problem in Ultrasound Tomography
Sažetak
Ultrasound tomography (UT) is a noninvasive imaging modality that could be used to detect breast cancer. When compared to standard imaging techniques such as X-ray mammography, UT is cheaper, safer and better discerns dense breast tissue. One of the ways to reproduce the UT image is to use the Distorted Born Iterative (DBI) method. However, within each iteration of DBI an ill-posed inverse problems needs to be solved. This is a difficult task since standard regularization methods are not proven to be effective in most cases. Therefore, we use Tikhonov regularization in general form with our novel algorithm for choosing a regularization parameter λ. We test in simulations the robustness of our algorithm to changes in frequency. In addition, we provide the modification of the algorithm to achieve better reconstruction when lower levels of noise are considered in the measured data. The algorithm's efficiency is compared to a standard algorithm for obtaining regularization parameter: Generalized Cross Validation (GCV).
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Računarstvo
POVEZANOST RADA
Projekti:
HRZZ-UIP-2019-04-5200 - Dekompozicije i aproksimacije matrica i tenzora (DAMAT) (Begović Kovač, Erna, HRZZ - 2019-04) ( CroRIS)
HRZZ-IP-2020-02-2240 - Matrični algoritmi u nekomutativnim asocijativnim algebrama (MANAA) (Slapničar, Ivan, HRZZ - 2020-02) ( CroRIS)
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split