Pregled bibliografske jedinice broj: 998817
Employing methods with generalized singular value decomposition for regularization in ultrasound tomography
Employing methods with generalized singular value decomposition for regularization in ultrasound tomography // Proceedings Volume 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography / Brett C. Byram, Nicole V. Ruiter (ur.).
San Diego (CA): SPIE, 2019. str. 1095509-1095515 doi:10.1117/12.2511630 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Employing methods with generalized singular value decomposition for regularization in ultrasound tomography
Autori
Carević, Anita ; Abdou, Ahmed ; Slapničar, Ivan ; Almekkawy, Mohamed
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings Volume 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography
/ Brett C. Byram, Nicole V. Ruiter - San Diego (CA) : SPIE, 2019, 1095509-1095515
Skup
SPIE Medical Imaging 2019
Mjesto i datum
San Diego (CA), Sjedinjene Američke Države, 16.02.2019. - 21.02.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Inverse problem, regularization, ultrasound tomography, distorted Born iterative method
Sažetak
The Distorted Born Iterative (DBI) method is used for ultrasound tomography in order to localize and identify malignant breast tissues. This approach begins with the Born approximation to generate an initial prediction of the scattering function. Then, iteratively solves the forward problem for the total eld and the inhomoge- neous Green's function, and the inverse problem for the scattering function. The drawback of this method is that the associated inverse scattering problem is ill-posed. We are proposing the Truncated General Singular Value Decomposition (TGSVD) approach as a regularization method for the ill posed inverse problem Xy = b in DBI and comparing it to the well known Truncated Singular Value Decomposition (TSVD). The TGSVD employs generalized SVD (GSVD) of matrix pair (X ; L) and is neglecting the smallest, contaminated with noise, generalized singular values, while regularization matrix L (we used rst order derivative operator) is responsible for smoothing the solution. This results in better image quality. We compared the performances of these two methods on simulated phantom and proved that TGSVD produces lower relative error and better reconstructed image.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Conference Proceedings Citation Index - Science (CPCI-S)
- Scopus