Azimi et al., 2019 - Google Patents
Iris recognition under the influence of diabetesAzimi et al., 2019
View PDF- Document ID
- 4274358682739906033
- Author
- Azimi M
- Rasoulinejad S
- Pacut A
- Publication year
- Publication venue
- Biomedical Engineering/Biomedizinische Technik
External Links
Snippet
In this study, iris recognition under the influence of diabetes was investigated. A new database containing 1318 pictures from 343 irides–546 images from 162 healthy irides (62% female users, 38% male users, 21%< 20 years old, 61%(20)< 40 years old, 12%(40)< …
- 210000000554 Iris 0 title abstract description 89
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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- 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
- G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
- G06K9/00604—Acquisition
-
- 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
- G06K9/00006—Acquiring or recognising fingerprints or palmprints
- G06K9/00013—Image acquisition
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/117—Identification of persons
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0476—Electroencephalography
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/44—Detecting, measuring or recording for evaluating the integumentary system, e.g. skin, hair or nails
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Computer‐assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network | |
EP3373798B1 (en) | Method and system for classifying optic nerve head | |
Zhang et al. | Sparse representation classifier for microaneurysm detection and retinal blood vessel extraction | |
L Srinidhi et al. | Recent advancements in retinal vessel segmentation | |
Noronha et al. | Automated classification of glaucoma stages using higher order cumulant features | |
Azimi et al. | Iris recognition under the influence of diabetes | |
Rosas-Romero et al. | A method to assist in the diagnosis of early diabetic retinopathy: Image processing applied to detection of microaneurysms in fundus images | |
Bansal et al. | Determining diabetes using iris recognition system | |
Chalakkal et al. | Automatic detection and segmentation of optic disc and fovea in retinal images | |
Illavarason et al. | Medical diagnosis of cerebral palsy rehabilitation using eye images in machine learning techniques | |
Rehman et al. | Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors | |
Uppamma et al. | Deep learning and medical image processing techniques for diabetic retinopathy: a survey of applications, challenges, and future trends | |
Szymkowski et al. | A novelty approach to retina diagnosing using biometric techniques with SVM and clustering algorithms | |
Mookiah et al. | Application of higher-order spectra for automated grading of diabetic maculopathy | |
Zou et al. | Learning‐Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image | |
Chen et al. | Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis | |
Samant et al. | Analysis of computational techniques for diabetes diagnosis using the combination of iris-based features and physiological parameters | |
Song et al. | Multiple facial image features-based recognition for the automatic diagnosis of turner syndrome | |
Cheng et al. | Similarity regularized sparse group lasso for cup to disc ratio computation | |
Raveenthini et al. | Multiocular disease detection using a generic framework based on handcrafted and deep learned feature analysis | |
Krishnan et al. | An integrated diabetic retinopathy index for the diagnosis of retinopathy using digital fundus image features | |
Bansal et al. | Utilization of big data classification models in digitally enhanced optical coherence tomography for medical diagnostics | |
Hájek et al. | Recognition-based on eye biometrics: Iris and retina | |
Gupta et al. | Comparative study of different machine learning models for automatic diabetic retinopathy detection using fundus image | |
Zhou et al. | A comprehensive multimodal eye recognition |