Brancati et al., 2018 - Google Patents
Automatic segmentation of pigment deposits in retinal fundus images of Retinitis PigmentosaBrancati et al., 2018
- Document ID
- 6170348427215843855
- Author
- Brancati N
- Frucci M
- Gragnaniello D
- Riccio D
- Di Iorio V
- Di Perna L
- Publication year
- Publication venue
- Computerized Medical Imaging and Graphics
External Links
Snippet
Retinitis Pigmentosa is an eye disease that presents with a slow loss of vision and then evolves until blindness results. The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of …
- 208000007014 Retinitis Pigmentosa 0 title abstract description 30
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
- G06T2207/30041—Eye; Retina; Ophthalmic
-
- 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
- G06T2207/10101—Optical tomography; Optical coherence tomography [OCT]
-
- 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
- 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
- G06K9/36—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
- 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
- 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
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Winder et al. | Algorithms for digital image processing in diabetic retinopathy | |
Marin et al. | Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images | |
Yin et al. | Vessel extraction from non-fluorescein fundus images using orientation-aware detector | |
Sánchez et al. | Retinal image analysis based on mixture models to detect hard exudates | |
Medhi et al. | An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images | |
Ramani et al. | Improved image processing techniques for optic disc segmentation in retinal fundus images | |
Akram et al. | Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy | |
Sarathi et al. | Blood vessel inpainting based technique for efficient localization and segmentation of optic disc in digital fundus images | |
Lam et al. | General retinal vessel segmentation using regularization-based multiconcavity modeling | |
Sánchez et al. | A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis | |
Mahendran et al. | Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms | |
Soomro et al. | Contrast normalization steps for increased sensitivity of a retinal image segmentation method | |
Siddalingaswamy et al. | Automatic localization and boundary detection of optic disc using implicit active contours | |
Jan et al. | Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration | |
WO2018116321A2 (en) | Retinal fundus image processing method | |
Llorens-Quintana et al. | A novel automated approach for infrared-based assessment of meibomian gland morphology | |
Jordan et al. | A review of feature-based retinal image analysis | |
Raja et al. | Performance analysis of retinal image blood vessel segmentation | |
Siddalingaswamy et al. | Automatic grading of diabetic maculopathy severity levels | |
Kanimozhi et al. | RETRACTED ARTICLE: Fundus image lesion detection algorithm for diabetic retinopathy screening | |
Brancati et al. | Automatic segmentation of pigment deposits in retinal fundus images of Retinitis Pigmentosa | |
Sidhu et al. | Segmentation of retinal blood vessels by a novel hybrid technique-Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE) | |
Biyani et al. | A clustering approach for exudates detection in screening of diabetic retinopathy | |
Escorcia-Gutierrez et al. | Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection | |
ManojKumar et al. | Feature extraction from the fundus images for the diagnosis of diabetic retinopathy |