[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Kulkarni et al., 2012 - Google Patents

Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework

Kulkarni et al., 2012

View PDF @Full View
Document ID
2375416198329532560
Author
Kulkarni R
Tuller M
Fink W
Wildenschild D
Publication year
Publication venue
Vadose Zone Journal

External Links

Snippet

Advancements in noninvasive imaging methods such as X-ray computed tomography (CT) have led to a recent surge of applications in porous media research with objectives ranging from theoretical aspects of pore-scale fluid and interfacial dynamics to practical applications …
Continue reading at acsess.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20092Interactive image processing based on input by user
    • G06T2207/20101Interactive definition of point of interest, landmark or seed
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/003Reconstruction from projections, e.g. tomography
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations

Similar Documents

Publication Publication Date Title
Kulkarni et al. Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework
US10643331B2 (en) Multi-scale deep reinforcement machine learning for N-dimensional segmentation in medical imaging
Sheppard et al. Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT
Alqahtani et al. Deep learning convolutional neural networks to predict porous media properties
Ali et al. Image segmentation for intensity inhomogeneity in presence of high noise
Brun et al. Pore3D: A software library for quantitative analysis of porous media
Ali et al. Deep learning based semantic segmentation of µCT images for creating digital material twins of fibrous reinforcements
Iassonov et al. Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures
Kaestner et al. Imaging and image processing in porous media research
Hashemi et al. A tomographic imagery segmentation methodology for three-phase geomaterials based on simultaneous region growing
Tuller et al. Segmentation of X‐ray CT data of porous materials: A review of global and locally adaptive algorithms
US20140161352A1 (en) Iterative method for determining a two-dimensional or three-dimensional image on the basis of signals arising from x-ray tomography
Gsaxner et al. Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data
Qadri et al. Vertebrae segmentation via stacked sparse autoencoder from computed tomography images
Mahdaviara et al. Deep learning for multiphase segmentation of X-ray images of gas diffusion layers
Welch et al. Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth
Thomik et al. Determination of 3D pore network structure of freeze-dried maltodextrin
Malik et al. 3D Quantum Cuts for automatic segmentation of porous media in tomography images
Stanberry et al. Boundary reconstruction in binary images using splines
Khan et al. Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples.
Schüle et al. Adaptive reconstruction of discrete-valued objects from few projections
Suzuki et al. Interactive segmentation of pancreases from abdominal CT images by use of the graph cut technique with probabilistic atlases
US20220398740A1 (en) Methods and systems for segmenting images
Cazasnoves et al. Statistical content-adapted sampling (SCAS) for 3D computed tomography
Staib Parametrically deformable contour models for image analysis