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Pregled bibliografske jedinice broj: 71696

Cardiac image segmentation using spatio-temporal clustering


Galić, Saša; Lončarić, Sven
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)


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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:

Avatar Url Sven Lončarić (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada

Citiraj ovu publikaciju:

Galić, Saša; Lončarić, Sven
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)
Galić, S. & Lončarić, S. (2001) Cardiac image segmentation using spatio-temporal clustering. U: Sonka, M. & Hanson, K. (ur.)Medical Imaging 2001; Image processing.
@article{article, author = {Gali\'{c}, Sa\v{s}a and Lon\v{c}ari\'{c}, Sven}, year = {2001}, pages = {1199-1206}, keywords = {spatio-temporal image segmentation, clustering, optical flow, image analysis, motion estimation}, title = {Cardiac image segmentation using spatio-temporal clustering}, keyword = {spatio-temporal image segmentation, clustering, optical flow, image analysis, motion estimation}, publisher = {The Society of Photo-Optical Instrumentation Engineers}, publisherplace = {San Diego (CA), Sjedinjene Ameri\v{c}ke Dr\v{z}ave} }
@article{article, author = {Gali\'{c}, Sa\v{s}a and Lon\v{c}ari\'{c}, Sven}, year = {2001}, pages = {1199-1206}, keywords = {spatio-temporal image segmentation, clustering, optical flow, image analysis, motion estimation}, title = {Cardiac image segmentation using spatio-temporal clustering}, keyword = {spatio-temporal image segmentation, clustering, optical flow, image analysis, motion estimation}, publisher = {The Society of Photo-Optical Instrumentation Engineers}, publisherplace = {San Diego (CA), Sjedinjene Ameri\v{c}ke Dr\v{z}ave} }




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