Pregled bibliografske jedinice broj: 10568
Radial Basis Function-based Image Segmentation using a Receptive Field
Radial Basis Function-based Image Segmentation using a Receptive Field // Proceedings of the Tenth Annual IEEE Symposium on Computer-Based Medical Systems / Plummer, Deborah (ur.).
Maribor, Slovenija: Institute of Electrical and Electronics Engineers (IEEE), 1997. str. 126-130 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 10568 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Radial Basis Function-based Image Segmentation using a Receptive Field
Autori
Kovačević, Domagoj ; Lončarić, Sven
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the Tenth Annual IEEE Symposium on Computer-Based Medical Systems
/ Plummer, Deborah - : Institute of Electrical and Electronics Engineers (IEEE), 1997, 126-130
Skup
Tenth Annual IEEE Symposium on Computer-Based Medical Systems
Mjesto i datum
Maribor, Slovenija, 11.06.1996. - 13.06.1996
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
image processing; image analysis; image segmentation;neural networks
Sažetak
This paper presents a novel method for CT head image automatic
segmentation. The images are obtained from patients having the
spontaneous intra cerebral brain hemorrhage (ICH). The results of the
segmentation are images partitioned into five regions of interest
corresponding to four tissue classes (skull, brain, calcifications and
ICH ) and background. Once the images are segmented it is possible to
calculate various hemorrhage region parameters such as its size,
position, etc. The segmentation is performed in three major steps. In
the first phase a feature extraction and normalization is performed
using a receptive field (RF). Experiments were performed to determine
the optimal RF structure. Pixels are classified in the second phase
using the radial basis function (RBF) artificial neural
network. Experiments with different RBF network topologies were
performed in order to determine the optimal basis functions, network
size and a training algorithm. The segmentation results obtained using
the RBF network were compared with results obtained by multi-layer
perceptron neural network (MLP). In the third phase the image regions
obtained by the RBF network were labeled using an expert system.
Experiments have shown that the proposed method successfully performs
image segmentation.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
