Çetingül et al., 2011 - Google Patents
Estimation of local orientations in fibrous structures with applications to the Purkinje systemÇetingül et al., 2011
View HTML- Document ID
- 18377723940840496166
- Author
- Çetingül H
- Plank G
- Trayanova N
- Vidal R
- Publication year
- Publication venue
- IEEE transactions on biomedical engineering
External Links
Snippet
The extraction of the cardiac Purkinje system (PS) from intensity images is a critical step toward the development of realistic structural models of the heart. Such models are important for uncovering the mechanisms of cardiac disease and improving its treatment and …
- 239000000835 fiber 0 abstract description 106
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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
-
- 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
- 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
- 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
- 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
-
- 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
-
- 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
- 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
- G06K2209/051—Recognition of patterns in medical or anatomical images of internal organs
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Carass et al. | Longitudinal multiple sclerosis lesion segmentation: resource and challenge | |
Li et al. | Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines | |
García-Lorenzo et al. | Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging | |
Jiang et al. | 3D brain tumor segmentation in multimodal MR images based on learning population-and patient-specific feature sets | |
Gooya et al. | GLISTR: glioma image segmentation and registration | |
Mahapatra | Analyzing training information from random forests for improved image segmentation | |
WO2021030629A1 (en) | Three dimensional object segmentation of medical images localized with object detection | |
Ciompi et al. | HoliMAb: A holistic approach for Media–Adventitia border detection in intravascular ultrasound | |
Sparks et al. | Explicit shape descriptors: Novel morphologic features for histopathology classification | |
Nitzken et al. | Improving full-cardiac cycle strain estimation from tagged CMR by accurate modeling of 3D image appearance characteristics | |
Dang et al. | Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation | |
Deng et al. | Graph cut based automatic aorta segmentation with an adaptive smoothness constraint in 3D abdominal CT images | |
Göçeri et al. | Fully automated liver segmentation from SPIR image series | |
Zhu et al. | Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing | |
JP2019518288A (en) | Change detection in medical image | |
Zeng et al. | Liver vessel segmentation based on centerline constraint and intensity model | |
Karimi et al. | A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging | |
Mohan et al. | Tubular surface segmentation for extracting anatomical structures from medical imagery | |
Barbieri et al. | DTI segmentation via the combined analysis of connectivity maps and tensor distances | |
Li et al. | Application of Clustering‐Based Analysis in MRI Brain Tissue Segmentation | |
Çetingül et al. | Estimation of local orientations in fibrous structures with applications to the Purkinje system | |
Knuth et al. | Semi-automatic tumor segmentation of rectal cancer based on functional magnetic resonance imaging | |
Firjani et al. | A new 3D automatic segmentation framework for accurate extraction of prostate from diffusion imaging | |
Xiao et al. | Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features | |
Xiao et al. | PET and CT image fusion of lung cancer with siamese pyramid fusion network |