Leung et al., 2006 - Google Patents
Novel approach for anterior chamber angle analysis: anterior chamber angle detection with edge measurement and identification algorithm (ACADEMIA)Leung et al., 2006
View HTML- Document ID
- 17100691125203351209
- Author
- Leung C
- Yung W
- Yiu C
- Lam S
- Leung D
- Tse R
- Tham C
- Chan W
- Lam D
- Publication year
- Publication venue
- Archives of Ophthalmology
External Links
Snippet
Objective To describe a novel approach to measuring anterior chamber angle dimensions and configurations. Methods Sixty-nine images were selected randomly from the ultrasound biomicroscopic image database to develop the algorithm. Thirty images were selected for …
- 210000002159 Anterior Chamber 0 title abstract description 50
Classifications
-
- 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
-
- 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
- G06F19/3431—Calculating a health index for the patient, e.g. for risk assessment
-
- 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
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
-
- 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
-
- 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
- A61B3/102—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for optical coherence tomography [OCT]
-
- 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
- A61B5/48—Other medical applications
-
- 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
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
-
- 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
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
-
- 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
- A61B5/0059—Detecting, measuring or recording for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0062—Arrangements for scanning
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Thompson et al. | Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans | |
Radhakrishnan et al. | Comparison of optical coherence tomography and ultrasound biomicroscopy for detection of narrow anterior chamber angles | |
Salz et al. | Select features of diabetic retinopathy on swept-source optical coherence tomographic angiography compared with fluorescein angiography and normal eyes | |
Durbin et al. | Quantification of retinal microvascular density in optical coherence tomographic angiography images in diabetic retinopathy | |
Narayanaswamy et al. | Diagnostic performance of anterior chamber angle measurements for detecting eyes with narrow angles: an anterior segment OCT study | |
Coan et al. | Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review | |
McGrory et al. | Towards standardization of quantitative retinal vascular parameters: comparison of SIVA and VAMPIRE measurements in the Lothian Birth Cohort 1936 | |
Sakata et al. | Assessment of the scleral spur in anterior segment optical coherence tomography images | |
Mirzania et al. | Applications of deep learning in detection of glaucoma: a systematic review | |
Wan et al. | Optical coherence tomography angiography compared with optical coherence tomography macular measurements for detection of glaucoma | |
Zangwill et al. | Discriminating between normal and glaucomatous eyes using the Heidelberg retina tomograph, GDx nerve fiber analyzer, and optical coherence tomograph | |
Devereux et al. | Anterior chamber depth measurement as a screening tool for primary angle-closure glaucoma in an East Asian population | |
Weinreb et al. | Detection of glaucoma with scanning laser polarimetry | |
Kim et al. | Spectral-domain optical coherence tomography for detection of localized retinal nerve fiber layer defects in patients with open-angle glaucoma | |
Wang et al. | Automated explainable multidimensional deep learning platform of retinal images for retinopathy of prematurity screening | |
Chan et al. | A standardized method for reporting changes in macular thickening using optical coherence tomography | |
Teixeira et al. | Automated gonioscopy photography for iridocorneal angle grading | |
Prager et al. | Association of glaucoma-related, optical coherence tomography–measured macular damage with vision-related quality of life | |
Wallace et al. | Accuracy of ROPtool vs individual examiners in assessing retinal vascular tortuosity | |
Wu et al. | Multivendor spectral‐domain optical coherence tomography dataset, observer annotation performance evaluation, and standardized evaluation framework for intraretinal cystoid fluid segmentation | |
Lee et al. | Computer classification of nonproliferative diabetic retinopathy | |
Mihalache et al. | Accuracy of an artificial intelligence chatbot’s interpretation of clinical ophthalmic images | |
Pons et al. | Assessment of retinal nerve fiber layer internal reflectivity in eyes with and without glaucoma using optical coherence tomography | |
Zhu et al. | Quantifying discordance between structure and function measurements in the clinical assessment of glaucoma | |
Lin et al. | Trend-based progression analysis for examination of the topography of rates of retinal nerve fiber layer thinning in glaucoma |