Literature DB >> 18451927

Automated analysis of differential interference contrast microscopy images of the foveal cone mosaic.

David H Wojtas1, Bing Wu, Peter K Ahnelt, Philip J Bones, R P Millane.   

Abstract

An algorithm is presented for processing and analysis of differential interference contrast (DIC) microscopy images of the fovea to study the cone mosaic. The algorithm automatically locates the cones and their boundaries in such images and is assessed by comparison with results from manual analysis. Additional algorithms are presented that analyze the cone positions to extract information on cone neighbor relationships as well as the short-range order and domain structure of the mosaic. The methods are applied to DIC images of the human fovea.

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Year:  2008        PMID： 18451927     DOI： 10.1364/josaa.25.001181

Source DB:  PubMed          Journal:  J Opt Soc Am A Opt Image Sci Vis        ISSN： 1084-7529            Impact factor:   2.129


  13 in total

1.  Semi-automated identification of cones in the human retina using circle Hough transform.

Authors:  Danuta M Bukowska; Avenell L Chew; Emily Huynh; Irwin Kashani; Sue Ling Wan; Pak Ming Wan; Fred K Chen
Journal:  Biomed Opt Express       Date:  2015-11-03       Impact factor: 3.732

2.  Influence of sampling window size and orientation on parafoveal cone packing density.

Authors:  Marco Lombardo; Sebastiano Serrao; Pietro Ducoli; Giuseppe Lombardo
Journal:  Biomed Opt Express       Date:  2013-07-12       Impact factor: 3.732

3.  The organization of the cone photoreceptor mosaic measured in the living human retina.

Authors:  Lucie Sawides; Alberto de Castro; Stephen A Burns
Journal:  Vision Res       Date:  2016-08-03       Impact factor: 1.886

4.  Comparison of confocal and non-confocal split-detection cone photoreceptor imaging.

Authors:  Nripun Sredar; Moataz Razeen; Bartlomiej Kowalski; Joseph Carroll; Alfredo Dubra
Journal:  Biomed Opt Express       Date:  2021-01-08       Impact factor: 3.732

5.  Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning.

Authors:  Morgan Heisler; Myeong Jin Ju; Mahadev Bhalla; Nathan Schuck; Arman Athwal; Eduardo V Navajas; Mirza Faisal Beg; Marinko V Sarunic
Journal:  Biomed Opt Express       Date:  2018-10-10       Impact factor: 3.732

6.  Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images.

Authors:  Robert F Cooper; Marco Lombardo; Joseph Carroll; Kenneth R Sloan; Giuseppe Lombardo
Journal:  Vis Neurosci       Date:  2016-01       Impact factor: 3.241

7.  Adaptive-optics imaging of human cone photoreceptor distribution.

Authors:  Toco Yuen Chui; Hongxin Song; Stephen A Burns
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2008-12       Impact factor: 2.129

Review 8.  Adaptive optics technology for high-resolution retinal imaging.

Authors:  Marco Lombardo; Sebastiano Serrao; Nicholas Devaney; Mariacristina Parravano; Giuseppe Lombardo
Journal:  Sensors (Basel)       Date:  2012-12-27       Impact factor: 3.576

9.  RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images.

Authors:  David Cunefare; Alison L Huckenpahler; Emily J Patterson; Alfredo Dubra; Joseph Carroll; Sina Farsiu
Journal:  Biomed Opt Express       Date:  2019-07-08       Impact factor: 3.562

10.  Automatic cone photoreceptor segmentation using graph theory and dynamic programming.

Authors:  Stephanie J Chiu; Yuliya Lokhnygina; Adam M Dubis; Alfredo Dubra; Joseph Carroll; Joseph A Izatt; Sina Farsiu
Journal:  Biomed Opt Express       Date:  2013-05-22       Impact factor: 3.732
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