Naslov
Information entropy measures and clustering improve edge detection in medical X-ray images

Autori
Hržić, Franko ; Jansky, Vanja ; Sušanj, Diego ; Gulan, Gordan ; Kožar, Ivica ; Jeričević, Željko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
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, 2018, 164-166

ISBN
978-953-233-097-7

Skup
41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2018)

Mjesto i datum
Opatija, Hrvatska, 21.05.2018. - 25.05.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Image edge detection ; Information entropy ; Entropy, Bones ; Kernel ; Biomedical imaging ; Digital images

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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