Application of the ordered logit model to optimising Frangi filter parameters for segmentation of perivascular spaces

Lucia Ballerini, R Lovreglio, Maria Valdes Hernandez, Victor Gonzalez-Castro, Susana Muñoz Maniega, Enrico Pellegrini, Mark Bastin, Ian Deary, Joanna Wardlaw

Research output: Contribution to journalArticlepeer-review

Abstract

Segmentation of perivascular spaces (PVS) from brain magnetic resonance images (MRI) is important for understanding the brain's lymphatic system and its relationship with neurological diseases. The Frangi filter might be a valuable tool for this purpose. However, its parameters need to be adjusted in response to the variability in the scanner's parameters and study protocols. Knowing the neuroradiological ratings of the PVS, we used the ordered logit model to optimise Frangi filter parameters. The PVS volume obtained significantly and strongly correlated with neuroradiological assessments (Spearman's ρ=0.75, p < 0.001), suggesting that the ordered logit model could be a good alternative to conventional optimisation frameworks for segmenting PVS on MRI.
Original languageEnglish
Pages (from-to)61-67
JournalProcedia Computer Science
DOIs
Publication statusPublished - 25 Jul 2016

Fingerprint Dive into the research topics of 'Application of the ordered logit model to optimising Frangi filter parameters for segmentation of perivascular spaces'. Together they form a unique fingerprint.

Cite this