Application of the Ordered Logit Model to Optimising Frangi Filter Parameters for Segmentation of Perivascular Spaces
نویسندگان
چکیده
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 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. c © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Organizing Committee of MIUA 2016.
منابع مشابه
Unsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملOn Rank-Ordered Nested Multinomial Logit Model and D-Optimal Design for this Model
In contrast to the classical discrete choice experiment, the respondent in a rank-order discrete choice experiment, is asked to rank a number of alternatives instead of the preferred one. In this paper, we study the information matrix of a rank order nested multinomial logit model (RO.NMNL) and introduce local D-optimality criterion, then we obtain Locally D-optimal design for RO.NMNL models in...
متن کاملUnsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کامل$L-$ordered Fuzzifying Convergence Spaces
Based on a complete Heyting algebra, we modify the definition oflattice-valued fuzzifying convergence space using fuzzy inclusionorder and construct in this way a Cartesian-closed category, calledthe category of $L-$ordered fuzzifying convergence spaces, in whichthe category of $L-$fuzzifying topological spaces can be embedded.In addition, two new categories are introduced, which are called the...
متن کاملFurther results on $L$-ordered fuzzifying convergence spaces
In this paper, it is shown that the category of $L$-ordered fuzzifying convergence spaces contains the category of pretopological $L$-ordered fuzzifying convergence spaces as a bireflective subcategory and the latter contains the category of topological $L$-ordered fuzzifying convergence spaces as a bireflective subcategory. Also, it is proved that the category of $L$-ordered fuzzifying conver...
متن کامل