Nothing Special   »   [go: up one dir, main page]

Beasley, 2012 - Google Patents

Semiautonomous medical image segmentation using seeded cellular automaton plus edge detector

Beasley, 2012

View PDF @Full View
Document ID
6721638336565955989
Author
Beasley R
Publication year
Publication venue
International Scholarly Research Notices

External Links

Snippet

Segmentations of medical images are required in a number of medical applications such as quantitative analyses and patient‐specific orthotics, yet accurate segmentation without significant user attention remains a challenge. This work presents a novel segmentation …
Continue reading at 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
    • G06T2207/10088Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/30172Centreline of tubular or elongated structure
    • 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
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • 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
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • 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
    • 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
    • G06K9/20Image acquisition
    • G06K9/34Segmentation of touching or overlapping patterns in the image field
    • G06K9/342Cutting or merging image elements, e.g. region growing, watershed, clustering-based techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
Bai et al. Semi-supervised learning for network-based cardiac MR image segmentation
US10896508B2 (en) System for segmentation of anatomical structures in cardiac CTA using fully convolutional neural networks
Larsson et al. Robust abdominal organ segmentation using regional convolutional neural networks
Harrison et al. Progressive and multi-path holistically nested neural networks for pathological lung segmentation from CT images
Lee et al. Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration
Dzyubachyk et al. Advanced level-set-based cell tracking in time-lapse fluorescence microscopy
Chen et al. Global minimum for a Finsler elastica minimal path approach
Smith et al. Segmentation and tracking of cytoskeletal filaments using open active contours
Chothani et al. Automated tracing of neurites from light microscopy stacks of images
Chen et al. A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching
US10147185B2 (en) Interactive segmentation
Wang et al. A broadly applicable 3-D neuron tracing method based on open-curve snake
Bibiloni et al. A survey on curvilinear object segmentation in multiple applications
CN108292435A (en) The spinal vertebrae positioning and segmentation based on study in 3D CT
Pawar et al. LungSeg-Net: Lung field segmentation using generative adversarial network
Xiao et al. Automatic optimal filament segmentation with sub-pixel accuracy using generalized linear models and B-spline level-sets
Sbalzarini Seeing is believing: quantifying is convincing: computational image analysis in biology
Guo et al. Deepcenterline: A multi-task fully convolutional network for centerline extraction
Lv et al. Vessel segmentation using centerline constrained level set method
Li et al. SparseTracer: the reconstruction of discontinuous neuronal morphology in noisy images
Hong et al. MMCL-Net: spinal disease diagnosis in global mode using progressive multi-task joint learning
Koyuncu et al. Object‐oriented segmentation of cell nuclei in fluorescence microscopy images
Liao et al. Progressive minimal path method for segmentation of 2D and 3D line structures
Wyburd et al. TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations
Kitamura et al. Data-dependent higher-order clique selection for artery–vein segmentation by energy minimization