46 research outputs found
Groupwise Multimodal Image Registration using Joint Total Variation
In medical imaging it is common practice to acquire a wide range of
modalities (MRI, CT, PET, etc.), to highlight different structures or
pathologies. As patient movement between scans or scanning session is
unavoidable, registration is often an essential step before any subsequent
image analysis. In this paper, we introduce a cost function based on joint
total variation for such multimodal image registration. This cost function has
the advantage of enabling principled, groupwise alignment of multiple images,
whilst being insensitive to strong intensity non-uniformities. We evaluate our
algorithm on rigidly aligning both simulated and real 3D brain scans. This
validation shows robustness to strong intensity non-uniformities and low
registration errors for CT/PET to MRI alignment. Our implementation is publicly
available at https://github.com/brudfors/coregistration-njtv
A multimodal deep learning framework using local feature representations for face recognition
YesThe most recent face recognition systems are
mainly dependent on feature representations obtained using
either local handcrafted-descriptors, such as local binary patterns
(LBP), or use a deep learning approach, such as deep
belief network (DBN). However, the former usually suffers
from the wide variations in face images, while the latter
usually discards the local facial features, which are proven
to be important for face recognition. In this paper, a novel
framework based on merging the advantages of the local
handcrafted feature descriptors with the DBN is proposed to
address the face recognition problem in unconstrained conditions.
Firstly, a novel multimodal local feature extraction
approach based on merging the advantages of the Curvelet
transform with Fractal dimension is proposed and termed
the Curvelet–Fractal approach. The main motivation of this
approach is that theCurvelet transform, a newanisotropic and
multidirectional transform, can efficiently represent themain
structure of the face (e.g., edges and curves), while the Fractal
dimension is one of the most powerful texture descriptors
for face images. Secondly, a novel framework is proposed,
termed the multimodal deep face recognition (MDFR)framework,
to add feature representations by training aDBNon top
of the local feature representations instead of the pixel intensity
representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary
to those acquired by the Curvelet–Fractal approach.
Finally, the performance of the proposed approaches has
been evaluated by conducting a number of extensive experiments
on four large-scale face datasets: the SDUMLA-HMT,
FERET, CAS-PEAL-R1, and LFW databases. The results
obtained from the proposed approaches outperform other
state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by
achieving new state-of-the-art results on all the employed
datasets
Properties of Graphene: A Theoretical Perspective
In this review, we provide an in-depth description of the physics of
monolayer and bilayer graphene from a theorist's perspective. We discuss the
physical properties of graphene in an external magnetic field, reflecting the
chiral nature of the quasiparticles near the Dirac point with a Landau level at
zero energy. We address the unique integer quantum Hall effects, the role of
electron correlations, and the recent observation of the fractional quantum
Hall effect in the monolayer graphene. The quantum Hall effect in bilayer
graphene is fundamentally different from that of a monolayer, reflecting the
unique band structure of this system. The theory of transport in the absence of
an external magnetic field is discussed in detail, along with the role of
disorder studied in various theoretical models. We highlight the differences
and similarities between monolayer and bilayer graphene, and focus on
thermodynamic properties such as the compressibility, the plasmon spectra, the
weak localization correction, quantum Hall effect, and optical properties.
Confinement of electrons in graphene is nontrivial due to Klein tunneling. We
review various theoretical and experimental studies of quantum confined
structures made from graphene. The band structure of graphene nanoribbons and
the role of the sublattice symmetry, edge geometry and the size of the
nanoribbon on the electronic and magnetic properties are very active areas of
research, and a detailed review of these topics is presented. Also, the effects
of substrate interactions, adsorbed atoms, lattice defects and doping on the
band structure of finite-sized graphene systems are discussed. We also include
a brief description of graphane -- gapped material obtained from graphene by
attaching hydrogen atoms to each carbon atom in the lattice.Comment: 189 pages. submitted in Advances in Physic
Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks
Quantum Spacetime Phenomenology
I review the current status of phenomenological programs inspired by
quantum-spacetime research. I stress in particular the significance of results
establishing that certain data analyses provide sensitivity to effects
introduced genuinely at the Planck scale. And my main focus is on
phenomenological programs that managed to affect the directions taken by
studies of quantum-spacetime theories.Comment: 125 pages, LaTex. This V2 is updated and more detailed than the V1,
particularly for quantum-spacetime phenomenology. The main text of this V2 is
about 25% more than the main text of the V1. Reference list roughly double
A randomised controlled trial testing a web-based, computer-tailored self-management intervention for people with or at risk for chronic obstructive pulmonary disease: a study protocol
Contains fulltext :
125231.pdf (publisher's version ) (Open Access)BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality. Effective self-management support interventions are needed to improve the health and functional status of people with COPD or at risk for COPD. Computer-tailored technology could be an effective way to provide this support. METHODS/DESIGN: This paper presents the protocol of a randomised controlled trial testing the effectiveness of a web-based, computer-tailored self-management intervention to change health behaviours of people with or at risk for COPD. An intervention group will be compared to a usual care control group, in which the intervention group will receive a web-based, computer-tailored self-management intervention. Participants will be recruited from an online panel and through general practices. Outcomes will be measured at baseline and at 6 months. The primary outcomes will be smoking behaviour, measuring the 7-day point prevalence abstinence and physical activity, measured in minutes. Secondary outcomes will include dyspnoea score, quality of life, stages of change, intention to change behaviour and alternative smoking behaviour measures, including current smoking behaviour, 24-hour point prevalence abstinence, prolonged abstinence, continued abstinence and number of quit attempts. DISCUSSION: To the best of our knowledge, this will be the first randomised controlled trial to test the effectiveness of a web-based, computer-tailored self-management intervention for people with or at risk for COPD. The results will be important to explore the possible benefits of computer-tailored interventions for the self-management of people with or at risk for COPD and potentially other chronic health conditions. DUTCH TRIAL REGISTER: NTR3421