OBJECTIVE: Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation. METHODS: The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification. RESULTS: On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches. CONCLUSIONS: The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.
OBJECTIVE: Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation. METHODS: The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification. RESULTS: On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches. CONCLUSIONS: The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.
Authors: Alan D Fleming; Sam Philip; Keith A Goatman; John A Olson; Peter F Sharp Journal: Invest Ophthalmol Vis Sci Date: 2006-03 Impact factor: 4.799
Authors: Michael D Abràmoff; Meindert Niemeijer; Maria S A Suttorp-Schulten; Max A Viergever; Stephen R Russell; Bram van Ginneken Journal: Diabetes Care Date: 2007-11-16 Impact factor: 19.112
Authors: Johannes Wolz; Heinrich Audebert; Inga Laumeier; Michael Ahmadi; Maureen Steinicke; Caroline Ferse; Georg Michelson Journal: Int Ophthalmol Date: 2016-03-26 Impact factor: 2.031
Authors: Muhammad Moazam Fraz; Alicja R Rudnicka; Christopher G Owen; Sarah A Barman Journal: Int J Comput Assist Radiol Surg Date: 2013-12-24 Impact factor: 2.924
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