[go: up one dir, main page]

Mantilla et al., 2019 - Google Patents

Discriminative dictionary learning for local LV wall motion classification in cardiac MRI

Mantilla et al., 2019

View PDF
Document ID
1932957597782535704
Author
Mantilla J
Paredes J
Bellanger J
Donal E
Leclercq C
Garreau M
Publication year
Publication venue
Expert systems with applications

External Links

Snippet

The characterization of cardiac function is of high clinical interest for early diagnosis and better patient follow-up in cardiovascular diseases. A large number of cardiac image analysis methods and more precisely in cine-Magnetic Resonance Imaging (MRI) have …
Continue reading at www.sciencedirect.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05—Recognition of patterns in medical or anatomical images

Similar Documents

Publication Publication Date Title
Haque et al. Deep learning approaches to biomedical image segmentation
Li et al. Dilated-inception net: multi-scale feature aggregation for cardiac right ventricle segmentation
US9968257B1 (en) Volumetric quantification of cardiovascular structures from medical imaging
Shahangian et al. Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure
Afshin et al. Regional assessment of cardiac left ventricular myocardial function via MRI statistical features
Ahirwar Study of techniques used for medical image segmentation and computation of statistical test for region classification of brain MRI
US8724866B2 (en) Multi-level contextual learning of data
Hu et al. Automatic segmentation of left ventricle from cardiac MRI via deep learning and region constrained dynamic programming
US20080292194A1 (en) Method and System for Automatic Detection and Segmentation of Tumors and Associated Edema (Swelling) in Magnetic Resonance (Mri) Images
Punithakumar et al. Regional heart motion abnormality detection: An information theoretic approach
Mahapatra Automatic cardiac segmentation using semantic information from random forests
Wu et al. Prostate segmentation based on variant scale patch and local independent projection
Zhu et al. Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing
Ge et al. Two-steps coronary artery segmentation algorithm based on improved level set model in combination with weighted shape-prior constraints
Hu et al. Fully Automatic initialization and segmentation of left and right ventricles for large-scale cardiac MRI using a deeply supervised network and 3D-ASM
Zhang et al. Rapid surface registration of 3D volumes using a neural network approach
Mantilla et al. Discriminative dictionary learning for local LV wall motion classification in cardiac MRI
Xu et al. A novel machine-learning algorithm to estimate the position and size of myocardial infarction for MRI sequence
Guerrero et al. Group-constrained manifold learning: Application to AD risk assessment
Tran et al. Empirical mode decomposition and monogenic signal-based approach for quantification of myocardial infarction from mr images
Gloger et al. Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences
Arega et al. Using polynomial loss and uncertainty information for robust left atrial and scar quantification and segmentation
Al-Kadi Spatio-temporal segmentation in 3-D echocardiographic sequences using fractional Brownian motion
Menze et al. Proceedings of the miccai challenge on multimodal brain tumor image segmentation (brats) 2012
Metaxas et al. Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics