Fundamenta Informaticae - Volume 163, issue 1

Authors: Brunetti, Sara | Dulio, Paolo | Frosini, Andrea | Rozenberg, Grzegorz

Article Type: Other

DOI: 10.3233/FI-2018-1728

Citation: Fundamenta Informaticae, vol. 163, no. 1, pp. v-xiv, 2018

Authors: Der Sarkissian, Henri | Viganò, Nicola | Batenburg, Kees Joost

Article Type: Research Article

Abstract: Computed Tomography (CT) is an imaging technique that allows to reconstruct volumetric information of the analyzed objects from their projections. The most popular reconstruction technique is the Filtered Back Projection (FBP). It has the advantage of being the fastest technique available, but also the disadvantage to require a high number of projections to retrieve good quality reconstructions. In this article we propose a segmentation method for tomographic volumes composed of few materials. Our method combines existing high-quality variational segmentation frameworks with the data consistency approach used in tomography and discrete tomography. We show that our algorithm performs well under high …noise level and with moderately low number of projections, and that the data consistency significantly improves the segmentation, at the cost of only one FBP reconstruction and forward projection. Show more

DOI: 10.3233/FI-2018-1729

Citation: Fundamenta Informaticae, vol. 163, no. 1, pp. 1-20, 2018

Authors: Duchi, Enrica | Guerrini, Veronica | Rinaldi, Simone

Article Type: Research Article

Abstract: In this paper we study the family of permutations avoiding the pattern 122+ 3 (trivially equivalent to those avoiding 1 23 ⎵ 4 ), which extend the popular 123-avoiding permutations. In particular we provide an algorithmic description of a generating tree for these permutations, that is a way to build every object of a given size n + 1 in a unique way by performing local modifications on an object of size n . Our algorithm leads to a direct bijection between 1 23 ⎵ 4 -avoiding permutations and valley-marked Dyck …paths. It extends a known bijection between 123-avoiding permutations and Dyck paths, and makes explicit the connection between these objects that was earlier obtained by Callan through a series of non-trivial bijective steps. In particular our construction is simple enough to allow for efficient exhaustive generation. Show more

Keywords: Pattern Avoiding Permutations, Generating Trees, 1-23-4 Avoiding Permutations, Valley Marked Dyck Paths

DOI: 10.3233/FI-2018-1730

Citation: Fundamenta Informaticae, vol. 163, no. 1, pp. 21-39, 2018

Authors: Feschet, Fabien | Lemaire, Jean-Jacques

Article Type: Research Article

Abstract: Deep Brain Stimulation (DBS) has proven its efficiency in the treatment of Parkinson’s disease or essential tremor. It requires precise localizations of targets for instance in the thalamus. Since deep brain structures have been shown to be hardly visible on T1 or T2 weighted imaging, most methods rely on atlas based comparison and registration. It is however possible to use direct targeting using a specific MRI sequence called WAIR (White Matter Attenuated Inversion Recovery) even on 1.5 Tesla MRI machine. The direct targeting facilitates the precise segmentation of deep brain structures needed to plan the trajectories of the electrodes for …the DBS. But this remains a tedious delineation necessarily done by a neurosurgeon to avoid misinterpretation of the images. In this paper, we propose to build an isotropic super-resolution image for WAIR imaging to facilitate precise direct targeting of anatomical structures in the deep brain. We present a method to perform the reconstruction of a high resolution isotropic WAIR volume from three acquisitions performed on a volunteer subject. The method is based on transfinite interpolation in convex cells of an hyperplane arrangement. Our results show promising quality reconstruction for the computation of a super-resolution WAIR. It allows unambiguous segmentation of the deep brain to be used in DBS surgery. Show more

Keywords: Transfinite interpolation, WAIR MRI Imaging, denoising, super-resolution

DOI: 10.3233/FI-2018-1731

Citation: Fundamenta Informaticae, vol. 163, no. 1, pp. 41-62, 2018

Authors: Mancini, Matteo | Cercignani, Mara

Article Type: Research Article

Abstract: Mapping the brain structure and function is one of the hardest problems in science. Different image modalities, in particular the ones based on magnetic resonance imaging (MRI) can shed more light on how it is organised and how its functions unfold, but a theoretical framework is needed. In the last years, using network models and graph theory to represent the brain structure and function has become a major trend in neuroscience. In this review, we outline how network modelling has been used in neuroimaging, clarifying what are the underlying mathematical concepts and the consequent methodological choices. The major findings are …then presented for structural, functional and multimodal applications. We conclude outlining what are still the current issues and the perspective for the immediate future. Show more

Keywords: Network models, graph thoery, neuroimaging, brain connectivity, connectome

DOI: 10.3233/FI-2018-1732

Citation: Fundamenta Informaticae, vol. 163, no. 1, pp. 63-91, 2018

Authors: Dulio, Paolo | Finotelli, Paolo | Frosini, Andrea | Pergola, Elisa | Presenti, Alice

Article Type: Research Article

Abstract: It is commonly accepted that the various parts of the human brain interact as a network at macroscopic, mesoscopic and microscopic level. Recently, different network models have been proposed to mime the brain behavior both at resting state and during tasks: Our study concerns one of those model that consider both the physical and functional connectivity as well as topological metrics of the brain networks. We provide evidence of the soundness of the model by means of a synthetic dataset based on the existing literature concerning the active cerebral areas at the resting state. Furthermore, we consider Ruzicka similarity measure …in order to stress the predictive capability of the model and provide a thresholding criterium. Some network statistics are finally provided. Show more

DOI: 10.3233/FI-2018-1733

Citation: Fundamenta Informaticae, vol. 163, no. 1, pp. 93-109, 2018