PURPOSE: Retinal images acquired by means of digital photography are often used for evaluation and documentation of the ocular fundus, especially in patients with diabetes, glaucoma or age-related macular degeneration. The clinical usefulness of an image is highly dependent on its quality. We set out to develop and evaluate an automatic method of evaluating the quality of digital fundus photographs. METHODS: A method for making a numerical quantification of image sharpness and illumination was developed using Matlab image analysis functions. Based on their sharpness and illumination measures, 1000 fundus photographs, randomly selected from a clinical database, were assigned to four predefined quality groups (not acceptable, acceptable, good, very good). Six independent observers, comprising three experienced ophthalmologists and three ophthalmic nurses with extensive experience in fundus image acquisition, classified a selection of 100 of these images into the corresponding quality groups. RESULTS: Automatic quality evaluation was more sensitive than evaluation by human observers in terms of ability to discriminate between good and very good images. The median concordance between the six human observers and the automatic evaluation was substantial (kappa = 0.64). CONCLUSIONS: The proposed method provides an objective quality assessment of digital fundus photographs which agrees well with evaluations made by qualified human observers and which may be useful in clinical practice.
PURPOSE: Retinal images acquired by means of digital photography are often used for evaluation and documentation of the ocular fundus, especially in patients with diabetes, glaucoma or age-related macular degeneration. The clinical usefulness of an image is highly dependent on its quality. We set out to develop and evaluate an automatic method of evaluating the quality of digital fundus photographs. METHODS: A method for making a numerical quantification of image sharpness and illumination was developed using Matlab image analysis functions. Based on their sharpness and illumination measures, 1000 fundus photographs, randomly selected from a clinical database, were assigned to four predefined quality groups (not acceptable, acceptable, good, very good). Six independent observers, comprising three experienced ophthalmologists and three ophthalmic nurses with extensive experience in fundus image acquisition, classified a selection of 100 of these images into the corresponding quality groups. RESULTS: Automatic quality evaluation was more sensitive than evaluation by human observers in terms of ability to discriminate between good and very good images. The median concordance between the six human observers and the automatic evaluation was substantial (kappa = 0.64). CONCLUSIONS: The proposed method provides an objective quality assessment of digital fundus photographs which agrees well with evaluations made by qualified human observers and which may be useful in clinical practice.
Authors: Aaron S Coyner; Ryan Swan; James M Brown; Jayashree Kalpathy-Cramer; Sang Jin Kim; J Peter Campbell; Karyn E Jonas; Susan Ostmo; R V Paul Chan; Michael F Chiang Journal: AMIA Annu Symp Proc Date: 2018-12-05
Authors: Aaron S Coyner; Ryan Swan; J Peter Campbell; Susan Ostmo; James M Brown; Jayashree Kalpathy-Cramer; Sang Jin Kim; Karyn E Jonas; R V Paul Chan; Michael F Chiang Journal: Ophthalmol Retina Date: 2019-01-31
Authors: Edem Tsikata; Inês Laíns; João Gil; Marco Marques; Kelsey Brown; Tânia Mesquita; Pedro Melo; Maria da Luz Cachulo; Ivana K Kim; Demetrios Vavvas; Joaquim N Murta; John B Miller; Rufino Silva; Joan W Miller; Teresa C Chen; Deeba Husain Journal: Transl Vis Sci Technol Date: 2017-03-13 Impact factor: 3.283
Authors: Christopher L Passaglia; Tia Arvaneh; Erin Greenberg; David Richards; Brian Madow Journal: Transl Vis Sci Technol Date: 2018-03-23 Impact factor: 3.283