A Bottom-Up Saliency Estimation Approach for Neonatal Retinal Images

Abstract

Retinopathy of Prematurity (ROP) is a potentially blinding disease occurring primarily in prematurely born neonates. Staging or classification of ROP into various stages is mainly dependant on the presence of ridge or demarcation line and its distance with respect to optic disc. Thus, computer aided diagnosis of ROP requires method to automatically detect the ridge. To this end, a new bottom up saliency estimation method for neonatal retinal images is proposed. The method consists of first obtaining a depth map of neonatal retinal image via an image restoration scheme based on a physical model. The obtain depth is then converted to a saliency map. Then the image is further processed to even out illumination and contrast variations and the border artifacts. Next, two additional saliency maps are estimated from the processed image using gradient and appearance cues. The obtained saliency maps are then fused by pixel-wise multiplication and addition operators. The obtained final saliency map facilitates the detection of demarcation line and is qualitatively shown to be more suitable for neonatal retinal images compared to the state of the art saliency estimation techniques. This method could thus serve as tool for improved and faster diagnosis. Additionally, we also explore the usefulness of saliency maps for the task of classification of ROP into four stages.