Design of automatic focusing fundus camera

Purpose. To report the morphological and functional changes associated with a regression of foveoschisis in a patient with X-linked retinoschisis (XLRS).Methods. A 42-year-old man with XLRS underwent genetic analysis and detailed ophthalmic examinations. Functional assessments included best-corrected visual acuity (BCVA), full-field electroretinograms (ERGs), and multifocal ERGs (mfERGs). Morphological assessments included fundus photography, spectral-domain optical coherence tomography (SD-OCT), and adaptive optics (AO) fundus imaging. After the baseline clinical data were obtained, topical dorzolamide was applied to the patient. The patient was followed for 24 months.Results. A reportedRS1gene mutation was found (P203L) in the patient. At the baseline, his decimal BCVA was 0.15 in the right and 0.3 in the left eye. Fundus photographs showed bilateral spoke wheel-appearing maculopathy. SD-OCT confirmed the foveoschisis in the left eye. The AO images of the left eye showed spoke wheel retinal folds, and the folds were thinner than those in fundus photographs. During the follow-up period, the foveal thickness in the SD-OCT images and the number of retinal folds in the AO images were reduced.Conclusions. We have presented the detailed morphological changes of foveoschisis in a patient with XLRS detected by SD-OCT and AO fundus camera. However, the findings do not indicate whether the changes were influenced by topical dorzolamide or the natural history.

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Diabetic Retinopathy as Detected Using Ophthalmoscopy, a Nonmyciriatic Camera and a Standard Fundus Camera

Ophthalmology ◽

10.1016/s0161-6420(85)34003-4 ◽

1985 ◽

Vol 92 (4) ◽

pp. 485-491 ◽

Cited By ~ 153

Author(s):

Ronald Klein ◽

Barbara E.K. Klein ◽

Michael W. Neider ◽

Larry D. Hubbard ◽

Stagy M. Meuer ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Fundus Camera

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Study of Occurrence of Cataract and their Surgical Management in Dogs

Indian Journal of Animal Research ◽

10.18805/ijar.b-4235 ◽

2021 ◽

Author(s):

Benudhar Mahanand ◽

Jayakrushna Das

Keyword(s):

Fundus Examination ◽

Slit Lamp ◽

Small Incision Cataract Surgery ◽

Fundus Camera ◽

Small Incision ◽

Pupillary Light ◽

Maze Test ◽

Different Types ◽

Schiøtz Tonometry ◽

Indirect Ophthalmoscope

Background: Present study investigated the occurrence of cataract in dogs presented to the Teaching Veterinary Clinical Complex (TVCC) and Department of Veterinary Surgery and Radiology under Odisha University of Agriculture and Technology, Bhubaneswar during a period of four months. During the study period, a total number of 3847 canine cases were registered at TVCC, out of which 23 cases registered under the RKVY project for different types of ophthalmic affections. There were only eight canine cataract cases with overall prevalence of 0.2% among total canines presented to TVCC and 34.78% of total canines presented with some sorts of ophthalmic affections. Methods: Menace reflex, pupillary light reflex, maze test, obstacle test, catoptrics test, Schirmer’s tear test (STT), Schiotz tonometry, Slit lamp bio microscopy and fundus examination -using indirect ophthalmoscope (IO) and fundus camera, were carried out in all the cases studied. Result: Occurrence of cataract was higher in Spitz (50%). Males had more incidences (62.5%) of cataract. The ages of the dogs were less than 10 years. B mode ultra-sonographic (USG) examination revealed increased echogenecity of lens in all cataract affected dogs while corneal opacification was found in two cases. Cataract was extracted by phaco-emulsification in three dogs and by small incision cataract surgery (SICS) technique in one dog.

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Imaging Mouse Retinal Ganglion Cells and Their Loss In Vivo by a Fundus Camera in the Normal and Ischemia-Reperfusion Model

Investigative Opthalmology & Visual Science ◽

10.1167/iovs.07-1211 ◽

2008 ◽

Vol 49 (12) ◽

pp. 5546 ◽

Cited By ~ 21

Author(s):

Hiroshi Murata ◽

Makoto Aihara ◽

Yi-Ning Chen ◽

Takashi Ota ◽

Jiro Numaga ◽

...

Keyword(s):

Retinal Ganglion Cells ◽

Ganglion Cells ◽

Ischemia Reperfusion ◽

Retinal Ganglion ◽

Fundus Camera

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Telemedicine Screening of Diabetic Retinopathy Using a Hand-Held Fundus Camera

Telemedicine Journal ◽

10.1089/107830200415153 ◽

2000 ◽

Vol 6 (2) ◽

pp. 219-223 ◽

Cited By ~ 32

Author(s):

Kanagasingam Yogesan ◽

Ian J. Constable ◽

Chris J. Barry ◽

Robert H. Eikelboom ◽

Ian L. McAllister ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Fundus Camera

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Reliability of Manual Vascular Segmentation for Retinal Fractal Dimension using Peek Retinatm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i3326.0789s319 ◽

2019 ◽

Vol 8 (9S3) ◽

pp. 1560-1564

Keyword(s):

Fractal Dimension ◽

Blood Vessel ◽

Blood Vessels ◽

Retinal Image ◽

Retinal Images ◽

Retinal Vasculature ◽

Manual Segmentation ◽

Good Reliability ◽

Fundus Camera ◽

Df Analysis

Fractal dimension (Df) has been identified as indirect measure in quantifying the complexity of retinal vessel network which is useful for early detection of vascular changes. Reliability studies of Df measurement on retinal vasculature, has been conducted on retinal images processed by using semi-automated software which only permits image with 45ᵒ field of view (FOV). Smartphone-assisted fundus camera retinal image has a maximum 30ᵒ FOV which warrant manual processing in measuring the Df. Retinal blood vessels need to be manually segmented to produce binary images for retinal vasculatures Df measurement. Therefore, this study was conducted to determine the intragrader and intergrader reliability of manual segmentation of the retinal vasculature Df measurement from retinal images taken using a smartphone-assisted fundus camera Forty-five retinal images were captured using the Portable Eye Examination Kit Retina (Peek Retina™, Peek Vision Ltd, UK). Suitable image for Df analysis were selected based on gradable retinal image criteria which included; i) good image focus, ii) centered position of optic nerve head (ONH) and iii) significant blood vessel visibility. The images were cropped 0.5 disc diameters away from disc margin and resized to 500x500 pixels using GNU Image Manipulation Program Version 2.8.18 (GIMP, The GIMP Team, United States). Retinal vessels were manually traced by using layering capabilities for blood vessel segmentation. Df values of segmented blood vessels were measured by using Image J (National Institutes of Health, USA) and its plugin software, FracLac Version 2.5. Intragrader and intergrader reliability was determined by comparing the Df values between; two readings measured one week apart by a grader and readings from two different graders, respectively, using intraclass correlation coefficient (ICC) and Bland-Altman graphical plots. Intragrader agreement for retinal Df showed good reliability with ICC of 0.899 (95% CI: 0.814–0.945). Bland Altman analysis indicated good agreement between Df values at different grading time (mean difference 0.0050; 95% CI:-0.0001–0.0101). Intergrader reliability for retinal Df was high with ICC of 0.814 (95% CI: 0.459–0.919). Bland Altman plot revealed good intergrader agreement for retinal Df between two graders with a bias value of 0.0158 (95% CI: 0.0092–0.0223). In conclusion, manual segmentation of retinal image captured by smartphone-assisted fundus camera has good reliability (0.75 < ICC < 0.9) for Df analysis to study the morphology of retinal vasculatures.

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An Automatic Focusing Algorithm Based on Blur Edge Width Detection in the Image

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.461.470 ◽

2012 ◽

Vol 461 ◽

pp. 470-473

Author(s):

Fa Quan Zhang ◽

Xin Yu Liu ◽

Guo Fu Wang ◽

Jin Cai Ye

Keyword(s):

Wavelet Transform ◽

Binary Image ◽

Control Center ◽

Automatic Focusing ◽

Center Area ◽

Computation Speed

An automatic focusing algorithm through detecting blur edge width via wavelet transform was proposed. To improve computation speed and fit for different scenes, the center area of the image and four golden points were selected as the focusing area. Wavelet module values of the focusing area were obtained through wavelet transform. The threshold to determine blur edge was selected by 50 percent of the wavelet module maximum in all wavelet module values. According to the threshold, the binary image of the blur edge was achieved. Area and girth of the blur edge were calculated respectively, and blur edge width was computed by area and girth. The control center computed steps according to blur edge width to adjust the lens, and the focusing motor operated corresponding steps received from the control center. Therefore, an automatic focusing procedure was implemented. Results show that the automatic focusing algorithm is quick and effective.

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