Evaluation of retinal image quality assessment networks in different color-spaces

Fu，Huazhu1; Wang，Boyang1; Shen，Jianbing1; Cui，Shanshan1; Xu，Yanwu3; Liu，Jiang2,3; Shao，Ling1

10.1007/978-3-030-32239-7_6

2019

0302-9743

1611-3349

LECT NOTES ARTIF INT

11764 LNCS

48-56

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems. Existing RIQA methods focus on the RGB color-space and are developed based on small datasets with binary quality labels (i.e., ‘Accept’ and ‘Reject’). In this paper, we first re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., ‘Good’, ‘Usable’ and ‘Reject’) for evaluating RIQA methods. Our RIQA dataset is characterized by its large-scale size, multi-level grading, and multi-modality. Then, we analyze the influences on RIQA of different color-spaces, and propose a simple yet efficient deep network, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades. Experiments on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art performance, outperforming the other deep learning methods. Furthermore, we also evaluate diabetic retinopathy (DR) detection methods on images of different quality, and demonstrate that the performances of automated diagnostic systems are highly dependent on image quality.

Deep learning Quality assessment Retinal image

其他

英语

CPCI-S ; EI

Computer Science ; Engineering ; Microscopy ; Neurosciences & Neurology ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Engineering, Biomedical ; Microscopy ; Neuroimaging ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000548734200006

20194807768282

Color ; Image analysis ; Deep learning ; Diagnosis ; Reliability analysis ; Ophthalmology ; Quality control ; Eye protection ; Image quality ; Large dataset

Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Data Processing and Image Processing:723.2 ; Light/Optics:741.1 ; Quality Assurance and Control:913.3 ; Accidents and Accident Prevention:914.1

2-s2.0-85075640913

Scopus

1.Inception Institute of Artificial Intelligence,Abu Dhabi,United Arab Emirates
2.Southern University of Science and Technology,Shenzhen,China
3.Cixi Institute of Biomedical Engineering,CAS,Ningbo,China

Fu，Huazhu,Wang，Boyang,Shen，Jianbing,et al. Evaluation of retinal image quality assessment networks in different color-spaces[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2019:48-56.