ArticleBiomedical optics express2015
Semi-automated identification of cones in the human retina using circle Hough transform.
Article in Biomedical optics express, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Automated cone photoreceptor detection using synthetic data and deep learning in confocal adaptive optics scanning laser ophthalmoscope images.Scientific reports · 2026Article
- From Cellular to Metabolic: Advances in Imaging of Inherited Retinal Diseases.Diagnostics (Basel, Switzerland) · 2024Review
- Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.Biomedical optics express · 2024Article
- Pearls and Pitfalls of Adaptive Optics Ophthalmoscopy in Inherited Retinal Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Quantification and Characterization of CTCs and Clusters in Pancreatic Cancer by Means of the Hough Transform Algorithm.International journal of molecular sciences · 2023Article
- Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects.Biomedical optics express · 2023Article
- Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina.Biomedical optics express · 2022Article
- Automatic quantification of cone photoreceptors in adaptive optics scanning light ophthalmoscope images using multi-task learning.Biomedical optics express · 2022Article
- Retinal Differential Light Sensitivity Variation Across the Macula in Healthy Subjects: Importance of Cone Separation and Loci Eccentricity.Translational vision science & technology · 2021Article
- Multimodal Retinal Imaging and Microperimetry Reveal a Novel Phenotype and Potential Trial End Points in CRB1-Associated Retinopathies.Translational vision science & technology · 2021Article
- Photoreceptor assessment in focal laser-treated central serous chorioretinopathy using adaptive optics and fundus autofluorescence.Medicine · 2020Observational
- Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks.Translational vision science & technology · 2019Article
- Cellular imaging of inherited retinal diseases using adaptive optics.Eye (London, England) · 2019Review
- Fully Automated Estimation of Spacing and Density for Retinal Mosaics.Translational vision science & technology · 2019Article
- RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2019Article
- Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly Constrained Active Contour Model.Investigative ophthalmology & visual science · 2018Article
- Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.Biomedical optics express · 2018Article
- The Reliability of Cone Density Measurements in the Presence of Rods.Translational vision science & technology · 2018Article
- Use of focus measure operators for characterization of flood illumination adaptive optics ophthalmoscopy image quality.Biomedical optics express · 2018Article
- Perifoveal interdigitation zone loss in hydroxychloroquine toxicity leads to subclinical bull's eye lesion appearance on near-infrared reflectance imaging.Documenta ophthalmologica. Advances in ophthalmology · 2018Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A large number of human retinal diseases are characterized by a progressive loss of cones, the photoreceptors critical for visual acuity and color perception. Adaptive Optics (AO) imaging presents a potential method to study these cells in vivo. However, AO imaging in ophthalmology is a relatively new phenomenon and quantitative analysis of these images remains difficult and tedious using manual methods. This paper illustrates a novel semi-automated quantitative technique enabling registration of AO images to macular landmarks, cone counting and its radius quantification at specified distances from the foveal center. The new cone counting approach employs the circle Hough transform (cHT) and is compared to automated counting methods, as well as arbitrated manual cone identification. We explore the impact of varying the circle detection parameter on the validity of cHT cone counting and discuss the potential role of using this algorithm in detecting both cones and rods separately.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.