Abstract
Leakage in retinal angiography currently is a key
feature for confirming the activities of lesions in the management
of a wide range of retinal diseases, such as diabetic maculopathy
and paediatric malarial retinopathy. This paper proposes a new
saliency-based method for the detection of leakage in fluorescein
angiography. A superpixel approach is firstly employed to divide
the image into meaningful patches (or superpixels) at different
levels. Two saliency cues, intensity and compactness, are then
proposed for the estimation of the saliency map of each individual
superpixel at each level. The saliency maps at different levels over
the same cues are fused using an averaging operator. The two
saliency maps over different cues are fused using a pixel-wise
multiplication operator. Leaking regions are finally detected by
thresholding the saliency map followed by a graph-cut segmentation.
The proposed method has been validated using the only
two publicly available datasets: one for malarial retinopathy and
the other for diabetic retinopathy. The experimental results show
that it outperforms one of the latest competitors and performs
as well as a human expert for leakage detection and outperforms
several state of the art methods for saliency detection.
Original language | English |
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Article number | 7518662 |
Pages (from-to) | 51-63 |
Number of pages | 13 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 36 |
Issue number | 1 |
Early online date | 21 Jul 2016 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Keywords
- Leakage
- diabetic
- fluorescein angiogram
- malarial
- retinopathy
- saliency
- segmentation
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YONGHUAI LIU
- Computer Science - Professor of Computer Games & Graphics
- Health Research Institute
Person: Research institute member, Academic