Jonathan R. T. Hughes

Hughes, Jonathan

doi:10.4324/9780203799635.ch13

Cited by 18 publications

(24 citation statements)

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“…In the future, automatically defined landmark localizations could be used for automatic creation of a discrete (Figure 13) and continuous (Figure 14) 3D spine models, which can be easily used in Finite Element Analysis [63–65], contrary to standard voxel representation.…”

Section: Discussionmentioning

confidence: 99%

Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model

Gaweł

Główka

Kotwicki

et al. 2018

BioMed Research International

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The study introduces a novel method for automatic segmentation of vertebral column tissue from MRI images. The paper describes a method that combines multiple stages of Machine Learning techniques to recognize and separate different tissues of the human spine. For the needs of this paper, 50 MRI examinations presenting lumbosacral spine of patients with low back pain were selected. After the initial filtration, automatic vertebrae recognition using Cascade Classifier takes place. Afterwards the main segmentation process using the patch based Active Appearance Model is performed. Obtained results are interpolated using centripetal Catmull–Rom splines. The method was tested on previously unseen vertebrae images segmented manually by 5 physicians. A test validating algorithm convergence per iteration was performed and the Intraclass Correlation Coefficient was calculated. Additionally, the 10-fold cross-validation analysis has been done. Presented method proved to be comparable to the physicians (FF = 90.19 ± 1.01%). Moreover results confirmed a proper algorithm convergence. Automatically segmented area correlated well with manual segmentation for single measurements (falser¯=normal0.8336) and for average measurements (falser¯=normal0.9068) with p = 0.05. The 10-fold cross-validation analysis (FF = 91.37 ± 1.13%) confirmed a good model generalization resulting in practical performance.

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Section: Discussionmentioning

confidence: 99%

Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model

Gaweł

Główka

Kotwicki

et al. 2018

BioMed Research International

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“…[4,5] Under these conditions, only olefins were transformed, which led to the development of very important industrial processes such as the Lummus ABB process, which converts ethylene into propylene through cross-metathesis with 2-butenes on W-based catalysts. [6] This discovery was rapidly turned into an alkane conversion process first by Banks (Philipps) and then by Hughes (Chevron) [7,8] by combining heterogeneous dehydrogenation/hydrogenation and olefin metathesis catalysts, which allows a given alkane to be converted into its lower and higher homologues. This reaction [Eq.…”

mentioning

confidence: 99%

From Olefin to Alkane Metathesis: A Historical Point of View

et al. 2006

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“…[18,19] The effectiveness of FEMs is evidenced by their use in commercial multiphysics software such as Comsol, [20] which has been used for the simulation of diverse types of plasma flow problems, such as glow, dielectric barrier, capacitive, and gliding arc discharges [21][22][23][24][25][26] ; as well as inductively coupled, direct current, and microwave discharges. [27] Among FEMs, so-called stabilized and Variational Multiscale (VMS) methods [28][29][30] have proven to be very versatile and robust, and particularly effective overcoming the challenges for arc discharge simulation listed above. Stabilized and VMS methods exemplify the state-of-the-art in computational mechanics.…”

Section: Arc Discharge Modeling and Simulationmentioning

confidence: 99%

Finite Element Methods for Arc Discharge Simulation

Trelles

2016

Plasma Processes & Polymers

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The numerical simulation of industrial arc discharge processes, such as plasma spraying, cutting, and welding, faces severe challenges that include resolution of multiscale features, multiphysics coupling, and robustness in the presence of large gradients. A review of Finite Element Methods (FEMs), particularly the Variational Multiscale (VMS) method, applied to arc discharge simulation is presented. The plasma is modeled as a compressible electromagnetic fluid in chemical equilibrium and thermodynamic nonequilibrium (NLTE). The effectiveness of the NLTE model and the VMS‐FEM is demonstrated with the simulation of canonical and industrially relevant arc discharges, namely the plasma flow in transferred and non‐transferred arc plasma torches and the free‐burning arc.

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Jonathan R. T. Hughes

Cited by 18 publications

References 0 publications

Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model

Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model

From Olefin to Alkane Metathesis: A Historical Point of View

Finite Element Methods for Arc Discharge Simulation

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