Research Papers: Imaging

Retinal image quality assessment based on image clarity and content

[+] Author Affiliations
Lamiaa Abdel-Hamid

Misr International University, Department of Electronics and Communication, Faculty of Engineering, Ismalia Road km28, Cairo, Egypt

Ahmed El-Rafei

Ain Shams University, Department of Engineering Physics and Mathematics, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt

Salwa El-Ramly

Ain Shams University, Department of Electronics and Communication, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt

Georg Michelson

Friedrich-Alexander University of Erlangen-Nuremberg, Department of Ophthalmology, Schwabachanlage 6, Erlangen 91054, Germany

Talkingeyes & More GmbH, Medical Valley Center, Erlangen 91052, Germany

Joachim Hornegger

Friedrich-Alexander University of Erlangen-Nuremberg, Pattern Recognition Lab, Department of Computer Science, Martensstr. 3, Erlangen 91058, Germany

J. Biomed. Opt. 21(9), 096007 (Sep 16, 2016). doi:10.1117/1.JBO.21.9.096007
History: Received March 22, 2016; Accepted August 29, 2016
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Abstract.  Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Lamiaa Abdel-Hamid ; Ahmed El-Rafei ; Salwa El-Ramly ; Georg Michelson and Joachim Hornegger
"Retinal image quality assessment based on image clarity and content", J. Biomed. Opt. 21(9), 096007 (Sep 16, 2016). ; http://dx.doi.org/10.1117/1.JBO.21.9.096007


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