Name
Abstract | ||
|---|---|---|
The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8036912 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
image quality classification, saliency map, convolutional neural networks | Computer vision,Saliency map,Pattern recognition,Convolutional neural network,Computer science,Human visual system model,Support vector machine,Fundus (eye),Image quality,Retinal image,Artificial intelligence,Deep learning | Conference |
Volume | ISSN | Citations |
2017 | 1094-687X | 1 |
PageRank | References | Authors |
0.36 | 5 | 6 |