Information entropy measures and clustering improve edge detection in medical X-ray images (CROSBI ID 664962)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Hržić, Franko ; Jansky, Vanja ; Sušanj, Diego ; Gulan, Gordan ; Kožar, Ivica ; Jeričević, Željko
engleski
Information entropy measures and clustering improve edge detection in medical X-ray images
Shannon information entropy measures and hierarchical agglomerative clustering were used to detect edges in digital images. The concept is based on communications theory with splitting of edge detection kernel into source and destination parts. The arbitrary shape of the kernel parts and the fact that information filter output is a real number with reduced problem of edge's continuity represents the major advantage of this approach. The methodology was applied globally (the same information entropy parameters were used on a whole image), and locally (adapting edge detection algorithm to localized, kernel size computed information context). The results indicate that using local information context could reduce the noise. The real life examples are taken from medical X-Ray imaging of series of femur bone in order to illustrate the algorithm performance on real data.
Image edge detection ; Information entropy ; Entropy, Bones ; Kernel ; Biomedical imaging ; Digital images
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Podaci o prilogu
164-166.
2018.
objavljeno
10.23919/mipro.2018.8400032
Podaci o matičnoj publikaciji
Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Skala, Karolj ; Koricic, Marko ; Galinac Grbac, Marko ; Cicin-Sain, Marina ; Sruk, Vlado ; Ribaric, Slobodan ; Gros, Stjepan ; Vrdoljak, Boris ; Mauher, Mladen ; Tijan, Edvard ; Pale, Predrag ; Janjic, Matej
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
978-953-233-097-7
Podaci o skupu
MIPRO 2018
predavanje
21.05.2018-25.05.2018
Opatija, Hrvatska