Pregled bibliografske jedinice broj: 1023070
Iterative Algorithms for Gaussian Mixture Model Estimation in 2D PET Imaging
Iterative Algorithms for Gaussian Mixture Model Estimation in 2D PET Imaging // Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis / Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko (ur.).
Zagreb: Sveučilište u Zagrebu, 2019. str. 93-99 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1023070 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Iterative Algorithms for Gaussian Mixture Model Estimation in 2D PET Imaging
Autori
Tafro, Azra ; Seršić, Damir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis
/ Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko - Zagreb : Sveučilište u Zagrebu, 2019, 93-99
ISBN
978-1-7281-3140-5
Skup
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
Mjesto i datum
Dubrovnik, Hrvatska, 23.09.2019. - 25.09.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
positron emission tomography, Gaussian mixture models, expectation-maximization (EM) algorithm, image segmentation
Sažetak
In positron emission tomography (PET) the original measurement consists of pairs of gamma rays emitted from a radioactive substance forming a line, captured within a given plane (2D) or volume (3D). Traditional image reconstruction methods estimate intensity values in pixels or voxels on some predefined grid. In this paper, we investigate reconstruction of PET images directly from the lines of response, using a probabilistic Gaussian mixture model (GMM) for the underlying originals. Parameters are estimated by solving an overdetermined system of equations obtained directly from measurements. Experiments are performed on artificial data, using an iterative process resembling the expectation-maximization algorithm. Reconstruction is performed using several variations of the algorithm, which are compared by measuring the structural similarity index of the graphic representation of underlying distributions. The proposed segmentation method relies on the statistical properties of GMMs and appears to be robust, giving new insight and a new approach to traditional problems on real data.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)
POVEZANOST RADA
Projekti:
HRZZ-IP-2014-09-2625 - Iznad Nyquistove granice (BeyondLimit) (Seršić, Damir, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Fakultet šumarstva i drvne tehnologije
