Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Yitian Zhao, Yalin Zheng, Yonghuai Liu, Jian Yang, Yifan Zhao, Duanduan Chen, Yongtian Wang

Research output: Contribution to journalArticlepeer-review

66 Citations (SciVal)
169 Downloads (Pure)


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 languageEnglish
Article number7518662
Pages (from-to)51-63
Number of pages13
JournalIEEE Transactions on Medical Imaging
Issue number1
Early online date21 Jul 2016
Publication statusPublished - 31 Jan 2017


  • diabetic
  • fluorescein angiogram
  • leakage
  • malarial
  • retinopathy
  • saliency
  • segmentation


Dive into the research topics of 'Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy'. Together they form a unique fingerprint.

Cite this