Information entropy measures and clustering improve edge detection in medical X-ray images
Franko Hržić, V. Jansky, Diego Sušanj, Gordan Gulan, Ivica Kožar, D. Z. Jericevic · 2018
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.