Literature DB >> 22894504

Anisotropy-based robust focus measure for non-mydriatic retinal imaging.

Andrés G Marrugo1, María S Millán, Gabriel Cristóbal, Salvador Gabarda, Héctor C Abril.   

Abstract

Non-mydriatic retinal imaging is an important tool for diagnosis and progression assessment of ophthalmic diseases. Because it does not require pharmacological dilation of the patient's pupil, it is essential for screening programs performed by non-medical personnel. A typical camera is equipped with a manual focusing mechanism to compensate for the refractive errors in the eye. However, manual focusing is error prone, especially when performed by inexperienced photographers. In this work, we propose a new and robust focus measure based on a calculation of image anisotropy which, in turn, is evaluated from the directional variance of the normalized discrete cosine transform. Simulation and experimental results demonstrate the effectiveness of the proposed focus measure.

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Year:  2012        PMID: 22894504     DOI: 10.1117/1.JBO.17.7.076021

Source DB:  PubMed          Journal:  J Biomed Opt        ISSN: 1083-3668            Impact factor:   3.170


  4 in total

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2.  Combination of Global Features for the Automatic Quality Assessment of Retinal Images.

Authors:  Jorge Jiménez-García; Roberto Romero-Oraá; María García; María I López-Gálvez; Roberto Hornero
Journal:  Entropy (Basel)       Date:  2019-03-21       Impact factor: 2.524

3.  Deep learning for gradability classification of handheld, non-mydriatic retinal images.

Authors:  Christos Bergeles; Sobha Sivaprasad; Paul Nderitu; Joan M Nunez do Rio; Rajna Rasheed; Rajiv Raman; Ramachandran Rajalakshmi
Journal:  Sci Rep       Date:  2021-05-04       Impact factor: 4.379

4.  Automated image curation in diabetic retinopathy screening using deep learning.

Authors:  Paul Nderitu; Joan M Nunez do Rio; Ms Laura Webster; Samantha S Mann; David Hopkins; M Jorge Cardoso; Marc Modat; Christos Bergeles; Timothy L Jackson
Journal:  Sci Rep       Date:  2022-07-01       Impact factor: 4.996

  4 in total

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