Pregled bibliografske jedinice broj: 71696
Cardiac image segmentation using spatio-temporal clustering
Cardiac image segmentation using spatio-temporal clustering // Medical Imaging 2001; Image processing / Sonka, Milan; Hanson, Kenneth M. (ur.).
San Diego (CA): The Society of Photo-Optical Instrumentation Engineers, 2001. str. 1199-1206 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 71696 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Cardiac image segmentation using spatio-temporal clustering
Autori
Galić, Saša ; Lončarić, Sven
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Medical Imaging 2001; Image processing
/ Sonka, Milan; Hanson, Kenneth M. - San Diego (CA) : The Society of Photo-Optical Instrumentation Engineers, 2001, 1199-1206
Skup
Medical Imaging 2001
Mjesto i datum
San Diego (CA), Sjedinjene Američke Države, 19.02.2001. - 22.02.2001
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
spatio-temporal image segmentation; clustering; optical flow; image analysis; motion estimation
Sažetak
Image segmentation is an important and challenging problem in image analysis. Segmentation of moving objects in image
sequences is even more difficult and computationally expensive. In this work we propose a technique for spatio -temporal
segmentation of medical sequences based on K-mean clustering in the feature vector space. The motivation for spatio-temporalsegmentation
approach comes from the fact that motion is a useful clue for object segme ntation. Two- dimensional
feature vector has been used for clustering in the feature space. In this paper we apply the proposed technique to
segmentation of cardiac images. The first feature used in this particular application is image brightness, which reveals the
structure of interest in the image. The second feature is the Euclidean norm of the optical flow vector. The third feature is
the three-dimensional optical flow vector, which consists of computed motion in all three dimensions. The optical flow
itself is computed using Horn-Schunck algorithm. The fourth feature is the mean brightness of the input image in a local
neighborhood. By applying the clustering algorithm it is possible to detect moving object in the image sequence. The
experiment has been conducted using a sequence of ECG-gated magnetic resonance (MR) images of a beating heart taken as
in time so in the space.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Projekti:
036024
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Sven Lončarić
(autor)