In this paper, we propose a novel approach to perform multi-label segmentation. Starting from an image, we construct the associated weighted graph and assign to a small number of pixels (seeds) a label. The aim is to assign each unlabeled pixel to a label in order to identify different regions. Our algorithm uses the notion of communicability from complex networks theory to compute the easiness for an unlabeled pixel to reach a labeled one. By assigning each pixel to the label for which the greatest communicability is calculated, we can perform good image segmentation.

Image segmentation by means of complex networks centrality indices

Fenu C.
2021

Abstract

In this paper, we propose a novel approach to perform multi-label segmentation. Starting from an image, we construct the associated weighted graph and assign to a small number of pixels (seeds) a label. The aim is to assign each unlabeled pixel to a label in order to identify different regions. Our algorithm uses the notion of communicability from complex networks theory to compute the easiness for an unlabeled pixel to reach a labeled one. By assigning each pixel to the label for which the greatest communicability is calculated, we can perform good image segmentation.
978-1-6654-5843-6
centrality indices
complex networks
image segmentation
medical imaging
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/335207
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