Guo et al., 2018 - Google Patents
A novel retinal vessel detection approach based on multiple deep convolution neural networksGuo et al., 2018
- Document ID
- 2542943742601677820
- Author
- Guo Y
- Budak Ã
- Şengür A
- Publication year
- Publication venue
- Computer methods and programs in biomedicine
External Links
Snippet
Background and objective Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detection is still a challenging …
- 210000001210 Retinal Vessels 0 title abstract description 45
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
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
-
- 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
- G06K9/36—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
- G06T2207/20112—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
- G06T2207/20076—Probabilistic image processing
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Guo et al. | A novel retinal vessel detection approach based on multiple deep convolution neural networks | |
Shorfuzzaman | An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection | |
Memari et al. | Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment | |
Jin et al. | DUNet: A deformable network for retinal vessel segmentation | |
Wang et al. | Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening | |
Dash et al. | A thresholding based technique to extract retinal blood vessels from fundus images | |
Orlando et al. | Convolutional neural network transfer for automated glaucoma identification | |
Adal et al. | Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning | |
Balasubramanian et al. | RETRACTED ARTICLE: Robust retinal blood vessel segmentation using convolutional neural network and support vector machine | |
Uysal et al. | Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks | |
Abbasi-Sureshjani et al. | Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images | |
Abbas et al. | DenseHyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning | |
Oliveira et al. | Augmenting data when training a CNN for retinal vessel segmentation: How to warp? | |
Soomro et al. | Contrast normalization steps for increased sensitivity of a retinal image segmentation method | |
Rasti et al. | Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier | |
Taie et al. | CSO-based algorithm with support vector machine for brain tumor's disease diagnosis | |
Tavakoli et al. | Automated detection of microaneurysms in color fundus images using deep learning with different preprocessing approaches | |
Thangavel et al. | EAD-DNN: Early Alzheimer's disease prediction using deep neural networks | |
Jaworek-Korjakowska | A deep learning approach to vascular structure segmentation in dermoscopy colour images | |
Singh et al. | Deep attention network for pneumonia detection using chest X-ray images | |
Kshirsagar et al. | Classification and detection of brain tumor by using GLCM texture feature and ANFIS | |
Bansal et al. | An improved hybrid classification of brain tumor MRI images based on conglomeration feature extraction techniques | |
Glorindal et al. | A simplified approach for melanoma skin disease identification | |
Al Jannat et al. | Detection of multiple sclerosis using deep learning | |
Ayomide et al. | Improving Brain Tumor Segmentation in MRI Images through Enhanced Convolutional Neural Networks |