Yin et al., 2013 - Google Patents
Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulationYin et al., 2013
View PDF- Document ID
- 3685444470053615905
- Author
- Yin Y
- Adel M
- Bourennane S
- Publication year
- Publication venue
- Computational and mathematical methods in medicine
External Links
Snippet
The automatic analysis of retinal blood vessels plays an important role in the computer‐ aided diagnosis. In this paper, we introduce a probabilistic tracking‐based method for automatic vessel segmentation in retinal images. We take into account vessel edge …
- 230000011218 segmentation 0 title abstract description 35
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
- G06T2207/10101—Optical tomography; Optical coherence tomography [OCT]
-
- 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
- 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
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- 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
- 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
- G06K9/36—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
- G06T2207/20212—Image combination
- G06T2207/20224—Image subtraction
-
- 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
- G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yin et al. | Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation | |
Imran et al. | Comparative analysis of vessel segmentation techniques in retinal images | |
Yin et al. | Retinal vessel segmentation using a probabilistic tracking method | |
Soomro et al. | Impact of image enhancement technique on CNN model for retinal blood vessels segmentation | |
Shukla et al. | A fractional filter based efficient algorithm for retinal blood vessel segmentation | |
Fu et al. | Joint optic disc and cup segmentation based on multi-label deep network and polar transformation | |
Zhao et al. | Retinal vessel segmentation: An efficient graph cut approach with retinex and local phase | |
Budai et al. | Robust vessel segmentation in fundus images | |
Yin et al. | Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping | |
Fraz et al. | Blood vessel segmentation methodologies in retinal images–a survey | |
Fraz et al. | Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification | |
Calvo et al. | Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images | |
Pachade et al. | NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation | |
Panda et al. | New binary Hausdorff symmetry measure based seeded region growing for retinal vessel segmentation | |
Chen et al. | Retinal image registration using topological vascular tree segmentation and bifurcation structures | |
Eladawi et al. | Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images | |
de Moura et al. | Joint diabetic macular edema segmentation and characterization in OCT images | |
Asad et al. | A new heuristic function of ant colony system for retinal vessel segmentation | |
Li et al. | DPF-Net: A dual-path progressive fusion network for retinal vessel segmentation | |
Muangnak et al. | Vessel transform for automatic optic disk detection in retinal images | |
Tavakoli et al. | Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction | |
Kumar et al. | Analysis of retinal blood vessel segmentation techniques: a systematic survey | |
Asl et al. | Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform | |
Chen et al. | Automated retinal layer segmentation in OCT images of age‐related macular degeneration | |
Lynn et al. | Melanoma classification on dermoscopy skin images using bag tree ensemble classifier |