Roberto Annunziata

TL;DR: Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements.

TL;DR: A fully automated framework for image-level tortuosity estimation, consisting of a hybrid segmentation method and a highly adaptable, definition-free tortuosity estimation algorithm, based on a novel tortUosity estimation paradigm in which discriminative, multi-scale features can be automatically learned for specific anatomical objects and diseases.

TL;DR: Experiments suggest that the proposed method reduces significantly the time taken to learn convolutional filter banks, and filters learned with the proposed strategy often achieve a much lower reconstruction error and match or exceed the segmentation performance of random and DCT-based initialisation, when used as input to a random forest classifier.

TL;DR: A novel ridge detector is proposed, SCIRD, which is simultaneously rotation, scale and curvature invariant, and relaxes shape assumptions to achieve enhancement of target image structures inSegmenting dendritic trees and corneal nerve fibres.

TL;DR: In general, performance may vary considerably with resampling, suggesting that the choice of a tortuosity index for clinical inference requires attention to numerical details, and ideally standardization thereof.