Liu et al., 2017 - Google Patents
A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus imagesLiu et al., 2017
View PDF- Document ID
- 16338118746944668093
- Author
- Liu Q
- Zou B
- Chen J
- Ke W
- Yue K
- Chen Z
- Zhao G
- Publication year
- Publication venue
- Computerized medical imaging and graphics
External Links
Snippet
The automatic exudate segmentation in colour retinal fundus images is an important task in computer aided diagnosis and screening systems for diabetic retinopathy. In this paper, we present a location-to-segmentation strategy for automatic exudate segmentation in colour …
- 210000000416 Exudates and Transudates 0 title abstract description 185
Classifications
-
- 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
- G06K9/36—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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
-
- 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
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- G06T2207/10024—Color image
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- G06K9/0061—Preprocessing; Feature extraction
-
- 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
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Liu et al. | A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images | |
Kaur et al. | A generalized method for the segmentation of exudates from pathological retinal fundus images | |
Akram et al. | Automated detection of exudates and macula for grading of diabetic macular edema | |
Imani et al. | A novel method for retinal exudate segmentation using signal separation algorithm | |
Garcia-Arroyo et al. | Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding | |
GeethaRamani et al. | Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis | |
Habib et al. | Detection of microaneurysms in retinal images using an ensemble classifier | |
Fraz et al. | Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification | |
Giancardo et al. | Exudate-based diabetic macular edema detection in fundus images using publicly available datasets | |
Sopharak et al. | Machine learning approach to automatic exudate detection in retinal images from diabetic patients | |
Arco et al. | Digital image analysis for automatic enumeration of malaria parasites using morphological operations | |
Adal et al. | Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning | |
Akram et al. | Identification and classification of microaneurysms for early detection of diabetic retinopathy | |
Imani et al. | Fully automated diabetic retinopathy screening using morphological component analysis | |
Pereira et al. | Using a multi-agent system approach for microaneurysm detection in fundus images | |
Villalobos-Castaldi et al. | A fast, efficient and automated method to extract vessels from fundus images | |
Ramakanth et al. | Approximate nearest neighbour field based optic disk detection | |
Punnolil | A novel approach for diagnosis and severity grading of diabetic maculopathy | |
Sharma et al. | Fuzzy c-means, anfis and genetic algorithm for segmenting astrocytoma-a type of brain tumor | |
Zaaboub et al. | Optic disc detection and segmentation using saliency mask in retinal fundus images | |
Wisaeng et al. | Improved fuzzy C-means clustering in the process of exudates detection using mathematical morphology | |
Verma et al. | Machine learning classifiers for detection of glaucoma | |
Jamil et al. | Computer based melanocytic and nevus image enhancement and segmentation | |
Elbalaoui et al. | Exudates detection in fundus images using mean-shift segmentation and adaptive thresholding | |
Zhou et al. | A novel approach for red lesions detection using superpixel multi-feature classification in color fundus images |