Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images
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Summary
This work presents a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model with more expressive potentials, and employs recent results enabling extremely fast inference in a fully connected model.
- Type
- article
- Published
- 2014-09-14
- Cited by
- 146
- References
- 30
- Access
- Open access
- OpenAlex
- https://openalex.org/W32702003
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:9155945
Keywords
Computer science, Conditional random field, CRFS, Segmentation, Artificial intelligence
References
- Synopsis of Clinical Ophthalmology
- Support vector learning
- Image Analysis and Recognition
- Markov Random Field Modeling in Image Analysis
- Supervised Feature Learning for Curvilinear Structure Segmentation
- Registration of OCT fundus images with color fundus photographs based on blood vessel ridges
- An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding
- Cutting-plane training of structural SVMs
- Segmentation of retinal blood vessels using the radial projection and semi-supervised approach
- Blood vessel segmentation methodologies in retinal images - A survey
- A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features
- Parallel Multiscale Feature Extraction and Region Growing: Application in Retinal Blood Vessel Detection
- Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
- Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction
- Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification
- Segmentation of blood vessels from red-free and fluorescein retinal images
- An effective retinal blood vessel segmentation method using multi-scale line detection
- Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images
- An improved matched filter for blood vessel detection of digital retinal images
- Retinal vessel tree segmentation using a deformable contour model
Cited by
- Region-Based Conditional Random Fields For Medical Image Labeling
- Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase
- Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images
- A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images
- A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction
- Vascular Tree Structure: Fast Curvature Regularization and Validation
- A quantum mechanics-based algorithm for vessel segmentation in retinal images
- A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images
- An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images
- B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising
- Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
- Structured Regression Gradient Boosting
- Performance comparison of publicly available retinal blood vessel segmentation methods
- A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding
- Automated blood vessel segmentation in fundus image based on integral channel features and random forests
- Retinal image analytics: a complete framework from segmentation to diagnosis.
- Advances in the theory and applications of (dis)similarity functions in pattern recognition
- Segment 2D and 3D Filaments by Learning Structured and Contextual Features
- Convolutional neural network transfer for automated glaucoma identification
- Integrated Gabor Filter and Trilateral Filter for Exudate Extraction in Fundus Images
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