Authors:
Bálint Antal
1
;
András Hajdu
1
;
Adrienne Csutak
1
and
Tünde Pető
2
Affiliations:
1
University of Debrecen, Hungary
;
2
Moorfields Eye Hospital, United Kingdom
Keyword(s):
Biomedical image processing, Medical decision-making, Quality assurance, Medical expert systems.
Related
Ontology
Subjects/Areas/Topics:
Design and Implementation of Signal Processing Systems
;
Image and Video Processing, Compression and Segmentation
;
Multimedia
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Neural Networks, Spiking Systems, Genetic Algorithms and Fuzzy Logic
;
Telecommunications
Abstract:
In this paper, we present an approach to decrease the computational burden of an automatic screening system designed for diabetic retinopathy. The proposed method consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on their abnormality. If an image is found to be abnormal, it will not be analyzed further with robust lesion detector algorithms. As an improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions which contain possible lesions for images that have been passed pre-screening. These regions will serve as inputs to lesion detectors later on, which can achieve better computational performance by operating on specific regions only instead of the entire image. Experimental results show that both two steps of the proposed approach are valid to efficiently exclude a large amount of data from further processing to improve the p
erformance of an automatic screening system.
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