A Two-phase Decision Support Framework for the Automatic Screening of Digital Fundus Images
| Autor: | Tünde Pető, Zsolt Török, Zsuzsanna Maros-Szabo, Adrienne Csutak, Andras Hajdu, Balint Antal |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
FOS: Computer and information sciences
Decision support system General Computer Science business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Detector Phase (waves) Computer Science - Computer Vision and Pattern Recognition Pattern recognition Fundus (eye) Medical decision making Theoretical Computer Science Image (mathematics) Modeling and Simulation Artificial intelligence business |
| Popis: | In this paper we give a brief review on the present status of automated detection systems describe for the screening of diabetic retinopathy. We further detail an enhanced detection procedure that consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on the severity of abnormalities. If an image is found to be seriously abnormal, it will not be analysed further with robust lesion detector algorithms. As a further improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions of interest with possible lesions on the images that previously passed the pre-screening step. These regions will serve as input to the specific lesion detectors for detailed analysis. This procedure can increase the computational performance of a screening system. Experimental results show that both two steps of the proposed approach are capable to efficiently exclude a large amount of data from further processing, thus, to decrease the computational burden of the automatic screening system. |
| Databáze: | OpenAIRE |
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